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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 25, 1605-1616 (2009)
1605
A New Perspective for Neural Networks: Application
to a Marketing Management Problem
JAESOO KIM AND HEEJUNE AHN+
Department of Computer Science and Engineering
+Department of Control and Instrumentation Engineering
Seoul National University of Technology
Seoul, 139-743 Korea
E-mail: heejune@snut.ac.kr
Over the last few years, connectionism or neural networks (NN) have successfully
been applied to a wide range of areas and have demonstrated their capabilities in solving
complex problems. Current indications show that these techniques are very important and
rapidly developing areas of research and applications, particularly, in the area of data
mining for knowledge discovery. One particular neural network model, the back-propa-
gation (BP) algorithm, has performed very well in this regard and it is now accepted as a
reliable method for data mining. However, these models have their shortcomings. The
major difficulty lies in the fact that the relationships between specific variables and the
neural network results are, at best, difficult to explain. This article presents an innovative
but simple method for using NN to understand the pattern/outcome correlation to inter-
pret a cause and effect relationship. A comparative analysis and experimental results are
also presented to show the validity of the proposed scheme.
Keywords: neural networks, sensitivity analysis, CART, logistic regression, data mining
1. INTRODUCTION
1.1 Background
We all know that information has become a very important commodity. Every sec-
ond hundreds of thousands of new records of information are generated. This informa-
tion needs to be summarized and synthesized if it is to support effective decision-making.
This involves the challenge of dealing with huge sets of data, dynamic data, incomplete
or imprecise data, noisy data and missing attributes, and redundant or insignificant data.
A successful approach to modeling non-linear relationships under these situations can be
usage of artificial neural networks (ANN) or connectionism that can be trained with the
set of available data [1, 8]. One particular neural network type, the back-propagation (BP)
algorithm has performed very well in this regard and it is now accepted as a reliable
method for data mining [2].
Neural networks share the advantages with the many other data mining tools. An
advantage they have over classical models used to analyzed data, such as regression
analysis, is that they can fit data where the relation between independent and dependent
variables is nonlinear and where the specific form of the nonlinear relationship is un-
known. Also, decision trees, a method of splitting data into homogeneous clusters with
Received October 19, 2007; revised April 9 & July 17, 2008; accepted August 28, 2008.
Communicated by Chin-Teng Lin.
+ Corresponding author.
JAESOO KIM AND HEEJUNE AHN
1606
similar expected values for the dependent variable, are often less effective when the pre-
dictor variables are continuous than when they are nominal (or categorical). Neural net-
works work well with both nominal and continuous variables. They do not require that
the relationships between predictor and dependent variables be linear whether or not the
variables are transformed. The neural network method is more robust and has better pre-
dictive accuracy than classical methods, such as discriminant and logistic analysis, in
many data mining applications. As the focus of this paper is neural networks, the other
data mining techniques will not be discussed further.
In spite of their advantages, neural networks with BP algorithm have their short-
comings. The major difficulty lies in the fact that the relationships between specific or
causal variables and the neural network results are, at best, difficult to explain because of
the complexity of the functions used in the neural network approximations. The output of
a neural network is a predicted value and some goodness of fit statistics. However, the
functional form of the relationship between predictor and target variables is not made
explicit. So the nature of the strength of the relationship between the independent and
dependent variables, i.e., the importance of each variable, is usually not revealed. Vali-
dating unexplainable results can be a significant challenge. This means there must be
something more general in their activity that led them to this result. Here we face the
challenge of finding appropriate way to figure out these interrelationships to make use of
them in the future without requiring some additional knowledge about the character of
the task.
Basically, the aim of this paper is to show that the neural network modeling may
offer significant advantages over the commonly used estimation procedures that can
summarise the large amount of collected data into relevant, concrete and effective action
recommendations for decision makers. In order to meet the growing demand of decision
makers, we have to focus on the systems that find adequate explanation models.
In this study, we also tested the comparative abilities of a neural network model, lo-
gistic regression, and classification and regression trees (CART) at capturing interrela-
tionship between the independent variables and the dependent variables. And this paper
describes how a model of factors influencing consumer behavior, from which initial
measures can be used, can be produced using a neural network based on consumer sur-
vey data.
1.2 Cultural Orientation and Consumer Behavior
The culturally based norms (appropriate behavior in a situation) and values (desir-
able behavior across situations) would lead to differences in consumer behavior across
cultures. These values and norms are passed on from the community to an individual as
he or she is socialized within the community. Consumers learn values and norms about
the acquisition, consumption and disposal of products through socialization in their
communities. Thus cultural values and norms become a primary explanation of similari-
ties in behavior of individuals within the community, and differences in the behavior of
individuals across communities [4].
Especially, Chinese consumer behavior is essentially different because of its unique
cultural, social and economic roots [13-15]. The behavior of Chinese consumers has
A NEW PERSPECTIVE FOR NEURAL NETWORKS
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even been distinguished from that of consumers in other Asian countries [5]. Sun &
Collins [13] studied consumers’ attitudes towards imported fruit in Guangzhou, finding
that fruit attributes in relation to symbolic and hedonic values were of primary impor-
tance in the decision to purchase.
Imported fruits have been widely available in China since about 1993. They are re-
tailed throughout the year in every major city in the country. Though imported fruit is
more expensive than locally produced fruit, there are still many willing buyers and the
imported fruit business has experienced burgeoning demand and high profits.
For this study, survey data were collected through structured intercept interviews
with consumers at point of sale immediately after they had purchased imported fruit.
Results will help to broaden our understanding of Chinese consumer behavior and pro-
vide valuable information when formulating marketing strategies.
The rest of the article is organized as follows. Section 2 reviews the studies on neu-
ral networks. In section 3, we introduce a sensitivity measure that assesses the relative
importance of the input factors used by the network to arrive at its targets and review the
existing relative importance measure for neural network input elements. In section 4, we
apply the method discussed in section 3 to a marketing management problem. Then, sec-
tion 5 compares and contrasts how neural networks and classical modeling techniques
deal with the specific modeling challenges and how the output of neural networks can be
used to better understand the relationship in the data through sensitivity analysis. Subse-
quently, we examine the results of our studies and test how the neural network model per-
forms in practice using the real-world data set. Finally, we draw conclusions in section 6.
2. NEURAL NETWORK AND ITS UNDERSTANDING THE OUTPUT
Neural networks are based on an early model of human brain function. Although it
is described as a network, a neural network is nothing more than a mathematical function
that computes an output based on a set of input variables. The network paradigm makes
it easy to decompose the larger function to a set of related subfunctions, and it enables a
variety of learning algorithms that can estimate the parameters of the subfunctions.
There are many different types of neural networks. A feedforward neural network
with one hidden layer considered in this paper is known as a multilayer perceptron
(MLP), which is one of the most popular kinds of neural networks and uses supervised
learning. As a result, its effectiveness has been established and software for applying it is
widely available. It has also been proved that a network with only one hidden layer is
enough to approximate any continuous function given there are enough nodes in the
hidden layer [3, 9]. The hidden layers are used to model the nonlinearities in the rela-
tionship between inputs and output [11]. Therefore neural networks might represent a
viable alternative to multivariate statistical methods.
Although neural networks can be applied to a number of data mining problems, in-
cluding classification, regression, and clustering, the complexity, combined with the non-
descriptive nature of neural network models, often discourages all but the most scientists
and researchers from employing the data mining technique.
Neural networks are trained by adjusting weights by some automatic learning algo-
rithms so that the result of stability approximates the desired outcomes for the provided
JAESOO KIM AND HEEJUNE AHN
1608
inputs. The output from neural networks varies greatly. Other common outputs are accu-
racy measures such as confusion matrix, R2, and so forth for validating the model. The
output from a neural network is purely predictive. Unfortunately, none of these aids the
user in understanding the model or the underlying data relationships. Because there is no
descriptive component to a neural network model, a neural network's choices are hard to
understand, and this often discourages its use. In fact, this technique is often referred to
as a black-box technology.
Because of the more complicated functions involved in neural network analysis, in-
terpretation of the variables is more challenging. One approach is to examine the weight
connecting the input variables to the hidden layer. Those which are closest to zero are
least important. A variable is deemed unimportant only if all of these connections are
near zero. This procedure is typically used to eliminate variables from a model, not to
quantify their impact on the outcome. Due to the homogeneous structure of neural net-
work, it is hard to extract structured knowledge from either the weights or the configura-
tion of the neural network in question. It should be emphasized that the weights in a neu-
ral network with hard-limiter as its activation function do have physical meaning [12].
The weights of a given node represent the coefficients of the hyperplane or discriminant
function that partitions the input space into two regions with different output values.
However, this interpretation of weights gets weaker and weaker if the net’s activation
function is either sigmoid or hyperbolic tangent functions and the given dependent vari-
ables are continuous instead of binary [10]. Therefore the weights are relatively unin-
formative for determining the influence of the variables on the fitted values.
Another approach to assessing the predictor variables’ importance is to compute a
sensitivity analysis for each variable. The sensitivity is a measure of how much the pre-
dicted value’s error increases when the variables are excluded from the model one at a
time. Through the sensitivity analysis, it is possible to generate an estimate of the general
level of influence exhibited by each parameter from an analysis of the network weights
in a systematic manner. This can be used to rank each variable’s importance. In the fol-
lowing section, a method for calculating output sensitivities to inputs’ variations from a
trained neural network is discussed in some detail.
3. SENSITIVITY ANALYSIS
In general, one of the key factors that affect the success of process modeling is the
ability to extract information about the model structure and the relationships between its
inputs and outputs from the trained network. Such information is essential for model
validation and for process optimization, control and safety assessments. Moreover, in
some cases where the original process is not well understood, this information can be
employed as a basis for the analysis of the process and in determining the most signifi-
cant factors that affect it.
For multilayer feedforward networks with n input nodes, one hidden layer with h
nodes and k output nodes the relative importance (RI) of the ith component of the input
vector can be estimated as follows:
A NEW PERSPECTIVE FOR NEURAL NETWORKS
1609
10
0
,
hji kj
n
jji
i
ik hji
j
=
=
=
=
∑∑
∑
ww
w
RI w (1)
where wji is the weight from the ith input node to the jth hidden node and wkj is the
weight from the jth hidden node to the kth output node. Biases are given the subscript 0.
Hence, the RI measure incorporates certain rates of change of the strengths of sig-
nals as they flow through the network. For example, wji is the partial derivatives of the
inputs to the hidden layer with respect to the inputs to the network. Similarly, wkj is the
partial derivatives of the inputs to the output layer with respect to the outputs of the hid-
den layer. So this RI measure is simply compounded weighted averages and is inde-
pendent of the activation function, therefore it is applicable to networks trained on a
range of activation functions, which are monotonically increasing.
Eq. (1) includes a component to normalize for the effect of extreme weights con-
necting input and hidden nodes. This additional component is also included in a closely
related formula given by Garson [7]:
11
111
.
ji kj
hn
jji
i
ik
ji kj
nh n
ij
j
iji
i
=
=
==
=
=
∑∑
∑∑ ∑
ww
w
RI ww
ww
(2)
Thus, for each j of h hidden nodes, sum the product formed by multiplying the in-
put-to-hidden connection weight of the input node i of variable for hidden node j, times
the connection weight of the output node k for hidden node j, then divide by the sum of
such quantities for all variables. The result is the percentage of all output weights attrib-
utable to the given independent variable, excepting bias weights arising from the back-
propagation algorithm.
However, the related method proposed by Garson does not include the effect of the
bias, which could result in a significant omission. Garson’s measure places more empha-
sis on the connection strengths from the hidden layer (wji) to the output layer (wkj), but it
does not measure the direction of influence (positive or negative). That is, during the
summation process, positive and negative weights can cancel their contribution or influ-
ence, which leads to inconsistent results. Including the bias influence allows all the influ-
ences to be considered in the context of the complete network. For instance, it is possible,
although unlikely, that the output of a network is based purely on the bias, and the input
signal has no significant effect. Using Garson's approach, the input parameters could be
assigned influence to various degrees since the overwhelming bias effect is ignored.
The RI measure given in this paper would illustrate the minimal (zero) influence of
the inputs and the large effect of the bias. In this way, it is possible that the denominator
in Eq. (1) will reduce to zero for non-zero weights. That is, the denominators will only
be zero if all the weights are zero, for instance all weights from the hidden layer to the
JAESOO KIM AND HEEJUNE AHN
1610
output layer are zero resulting in a network which simply outputs a single value deter-
mined by the activation function for any input signal or for all weights from the input
node under consideration to the hidden layer, including the bias, to be zero in which case
the input parameter will have no effect. Also, note that the numerator can become zero
under the same conditions, i.e., non-zero weights. In this case the strength of the input
would be zero. In the following section, it will be shown how this method can be applied
to a real world problem.
4. APPLICATION EXAMPLE
4.1 Data Collection and Statistical Information
For this study, a supermarket in China was chosen and the survey conducted was
mall-intercept personal interviews. The shop in its fruit section was deliberately divided
commodities into two subsections, domestic fruit and imported fruit, and also marked in
both sides with clear sign. It was a heavily trafficked store, and its management gave
approval to promote the survey as being on behalf of the company.
The purpose of the survey was explained to interviewees as being for the improve-
ment of service so that customers’ needs could be better understood and met. This was in
fact true because the company wanted to use the results. The rationale for this approach
was to ensure that the interviewee’s personal interest was directly associated with the
quality of their answers to the questions. Surveys were administered so as to avoid public
holidays and to achieve a spread across weekdays. The survey involved 520 personal
interviews in Guangzhou and a total of 495 useable responses was recorded for the study.
Respondents were asked about both their beliefs and evaluation of the imported
fruit, including their intention of purchasing imported fruit on each statement. 11 ques-
tions relating to consumers’ attitudes and perceptions that might be motivated their buy-
ing intention to imported fruit were designed.
The questions that consumers were asked in relation to these 11 attributes were
framed into statements according to Fishbein’s theory [6]. Fishbein’s proposition is that
people form attitudes towards a product attribute on the basis of their belief about that
attribute (comprised of perceptions and knowledge) and their positive or negative feel-
ings towards that attribute (comprised of their evaluation of that belief). According to
Fishbein, a consumer’s overall attitude toward imported fruit products would be repre-
sented by the sum of the products of their beliefs about each attribute and their evalua-
tion of those beliefs.
Among them, seven were the objective characteristics relating to attributes and per-
ceptions of imported products, such as appearance, packing, pollution, taste good, taste
different, freshness and price. Four were the subjective attributes towards symbolic
means of purchasing imported fruit products: achievement; wealthy; personality and so-
cial statues. Besides assessing consumers’ attitudes and perceptions of imported fruit a
behavioral response measure of consumer intention was elicited.
Table 1 describes some features of the independent variables and the dependent
variables used in this work. Note that there is no missing for the variables. Some descrip-
tive statistics for the data set of the consumers’ attitudes and perceptions towards im-
A NEW PERSPECTIVE FOR NEURAL NETWORKS
1611
Table 1. Description of attributes: the independent variables (x1 ~ x11) and the dependent
variable (y).
Attributes Variable Type
Appearance of fruit (x1) continuous
Packing status (x2) continuous
Less pollution (x3) continuous
Taste good (x4) continuous
Taste different (x5) continuous
Freshness (x6) continuous
Person’s achievement (x7) continuous
Wealthy (x8) continuous
Personality (x9) continuous
Social status (x10) continuous
Attitude to price (x11) categorical
Buying intention (y) categorical
Table 2. Descriptive statistics: each value is the product of beliefs by evaluation.
Attributes Mean Std. Dev. Skewness
Appearance of fruit 5.54 4.39 1.76
Packing status 5.70 4.78 1.73
Less pollution 7.60 5.57 1.00
Taste good 7.72 5.91 1.08
Taste different 6.80 5.53 1.51
Freshness 6.42 5.45 1.26
Person’s achievement 13.51 7.78 0.18
Wealthy 13.64 7.77 0.17
Personality 12.37 7.98 0.33
Social status 13.91 8.06 0.08
Attitude to price 1.71 0.46 − 0.91
ported fruit are also illustrated in Table 2. The data in this example have skewness values
of ranging from 0 to 2, which are considered acceptable for this task so that approximate
normality is attained after the data is logged.
4.2 Methods
The data set consists of 495 respondents on consumer survey in an imported fruit
market. The cross-validation procedures used in the neural network simulation were ap-
plied to the questionnaire data to prevent overfitting; that is 30 percent of the sample was
used to train the network, 20 percent was used to determine a stopping point for training,
and the remaining 50 percent was used for hold-out-sample testing of the predictive ac-
curacy. For the logistic regression and the CART modeling, the data was separated into a
training set of 248 customers and a test set of 247 customers. Variables used are de-
scribed in Table 1.
JAESOO KIM AND HEEJUNE AHN
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The logistic regression coefficients correspond to “B” coefficients in the logistic re-
gression equation indicate the amount of change expected in the log odds when there is a
one unit change in the predictor variable with all of the other variables in the model held
constant. A coefficient close to 0 suggests that there is no change due to the predictor
variable. There is a relationship between the logistic coefficients and the odds ratios,
odds ratio = Exp(B). These coefficients are used to compare the relative importance of
the independent variables in this work as it can be seen in Table 3.
Table 3. Coefficients of the relative importance to the various imported fruit character-
istics.
Attr Logistic Exp(B) (rank) NN RI (rank) CART Scores (%) (rank)
x1 1.062 (3) 0.015 (11) 3.71 (10)
x2 0.928 (10) 0.072 (7) 0.00 (11)
x3 1.060 (4) 0.266 (2) 27.55 (3)
x4 1.107 (2) 0.227 (3) 100.0 (1)
x5 0.996 (8) 0.066 (8) 25.22 (4)
x6 0.926 (11) 0.028 (10) 8.21 (9)
x7 1.030 (5) 0.111 (4) 22.35 (6)
x8 1.019 (6) 0.093 (5) 22.14 (7)
x9 0.991 (9) 0.042 (9) 11.34 (8)
x10 1.005 (7) 0.087 (6) 24.50 (5)
x11 2.073 (1) 0.269 (1) 57.38 (2)
For building a neural network model, we only considered a feedforward with a sin-
gle hidden layer architecture, as they can approximate any continuous function and train-
ing algorithm was the Lavenberg-Marquardt algorithm. The size of hidden nodes needs
to be only a relatively small fraction of the input layer. In this study, one empirical guide-
line is to determine the number of hidden nodes as twice the square root of the sum of
input and output nodes, design multiple networks by varying the initial weights, and use
the validation set to choose the best network. If the network fails to converge to a solu-
tion, it may be that more hidden nodes are required. If it does converge, we may try
fewer hidden nodes. Application of the fitted model to the test data indicated that a 4
node neural network provided the best model (i.e., 11-4-1). The performance measures
of each neural network model such as MSE, and classification accuracy (%) are the av-
erage of 5 trials.
To calculate a variable importance score, CART looks at the improvement measure
attributable to each variable in its role as a surrogate to the primary split. The values of
these improvements are summed over each node and totaled, and are scaled relative to
the best performing variable. In such ways, CART automatically produces the variable
importance ranking (scores) based on the contribution predictors make to the construc-
tion of the tree. The variable importance rankings or predictor rankings (%) are strictly
relative to a specific tree; change the tree and we might get very different rankings.
A NEW PERSPECTIVE FOR NEURAL NETWORKS
1613
5. COMPARATIVE ANALYSIS AND RESULTS
In this example, we attempt to assess the relative importance of input or causal vari-
ables by examining a trained neural network model using the real-world data. A neural
network with 4 nodes in the hidden layer was run on a training and validation set. Each
of the three models was tested on consumer survey data and used to rank variables in
importance.
Table 3 displays the results of the sensitivity test for each of the variable and shows
the coefficients of the models developed with the logistic regression method, ranking
scores (%) with the CART method and the neural network method using Eq. (1). The
table shows that x11 (price) is the most important input factor, followed closely by x3 (less
pollution); x4 (taste good) is of substantial but somewhat lesser importance. On the basis
of this measure, we could also correctly infer that in the case of subjective characteristics,
x7 (achievement) is the most likely direct cause of buying behavior and that x8 (wealthy)
are slightly less important but still substantial causes of the consumer’s buying behavior.
The odds ratio (or
β
weights or Exp(B)) in regression is interpreted as the ratio of
the relative importance of the causal or input variables in the model. As indicated by the
Exp(B) for the odds ratio in the table, x11, x4, x1, x3 and x7 have the highest importance in
affecting the choice of the consumer’s buying strategy. For this example, the odds ratio
of x11 was 2.073, 1.107 was x4, and 1.062 was x1. On the basis of regression, we would
correctly infer that x11 was the most important and likely direct cause of buying intention
and that x4, x1, x3 and x7 were equal but lesser causes. This similarly matches with the
results from the neural network analysis.
In the CART model case, the scores (%) reflect the contribution each variable
makes in classifying or predicting the dependent variable, with the contribution stem-
ming from both the variable’s role as a primary splitter and its role as a surrogate to any
of the primary splitters. In this example, x4, the variable used to split the root node, is
ranked as most important. The variable, x2, received a zero score, indicating that this
variable did not play any role in the analysis as either as primary splitters or surrogates.
x11 (price) also is an important splitting variable and has the highest scores, followed by
x3, x5, x10 and x7 as illustrated in Table 3.
For all three models’ measures, x11 has the highest contribution. For both our meas-
ure and in logistic regression’s measure, x3 and x4 were second, respectively, whereas in
CART, x11 was second, Thus, the logistic and neural network methods identify x11 as the
most important variable, whereas the CART model identifies x4 as the most important
variable.
According to the sensitivities, x3, x4 and x11 are the most important variables and x1
is the rather least important. This contrasts with the importance rankings of x1 in the lo-
gistic analysis, where x1 was a more important variable than others. Note that these are
the sensitivities for the particular models. A different initial starting point for the neural
network or a different number of hidden nodes could result in a model with different
sensitivities and the rankings can be quite sensitive to random fluctuations in the data.
The importance rankings in CART need to be understood as being relative to a particular
tree and the rankings are strictly relative to a given tree structure. Overall, it shows that
the consumer’s objective characteristics were more important than the subjective charac-
teristics in effecting the consumers’ purchasing behavior. The low sensitivities were
JAESOO KIM AND HEEJUNE AHN
1614
probably a result of the high correlations of the variables with each other.
These findings have obvious managerial implications since the consumers’ percep-
tions of the various imported fruit characteristics can be influenced by managerial ac-
tions. For some variables (e.g., packing status and freshness), the financial costs needed
to change consumers’ perceptions might be large, but these results show that it may have
little effect on consumer’s purchasing intention and should therefore not be a priority
item for managerial action. Conversely, since the ultimate purchasing behavior is more
strongly influenced by other variables, such as price/taste good/less pollution, manage-
rial actions affecting the consumers’ perceptions and attitudes of the imported fruit selec-
tion can better influence consumer’s buying expectations in the store.
Finally, the classification results for all three models are presented in Table 4. The
cross-validation procedure described earlier was used for all three models. The results in
Table 4 demonstrate that once again the neural network model exhibits a superior ability
to learn the patterns corresponding to consumer choice (buying intention). Consistent
with the simulation results, the neural networks demonstrate significantly better hold-out-
sample predictive accuracy that of the other models.
Table 4. Summary of classifications of the consumers’ attitudes and perceptions by lo-
gistic regression, neural network and CART.
Analysis Method Training Hold-Out
Logistic Regression 67.7% 63.56%
Neural Network 89.59% 65.18%
CART 82.66% 61.13%
6. CONCLUSIONS
In this paper, we have developed a method for determining the relative importance
of each input or causal variable of a neural network on the target. We have then applied
this method to a neural network model of an empirical examination of consumer’s be-
havior and showed that the neural network models can be used to improve the predic-
tions to an important business management problem as well as understand the relative
causal importance and order of the input variables.
Neural networks seem to have an advantage over linear models when they are ap-
plied to complex nonlinear data and may outperform classical models in certain situa-
tions, but interpreting the result is difficult because the nature of the relationship between
dependent and target variables is not usually revealed. Neural networks also have no
problem with trigonometric or logarithmic relationships, but either of these could be a
real problem for the other techniques. This is an advantage neural networks share with
other data mining tools not discussed in detail in this paper.
A method for interpreting the results of neural networks is presented here and in-
corporating such method into neural network models would help address the limitation.
Our RI measure does provide a reasonable method of using neural network for modeling
as well as for classification or prediction, and stands in sharp contrast to misleading
views of neural networks as black-boxes whose iterative processes are beyond human
compre-hension, even if the predictions are good.
A NEW PERSPECTIVE FOR NEURAL NETWORKS
1615
REFERENCES
1. C. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, New
York, 1995.
2. K. J. Cios, W. Pedrycz, and R. W. Swiniarski, Data Mining Methods for Knowledge
Discovery, Kluwer Academic Publishers, Dordrecht, 1998.
3. G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathe-
matics of Control, Signals and Systems, Vol. 2, 1989, pp. 303-314.
4. G. J. Tellis and D. S. Ackerman, “Can culture affect prices? A cross-cultural study
of shopping and retail prices,” Journal of Retailing, Vol. 77, 2001, pp. 57-82.
5. J. X. Fan and J. J. Xiao, “Consumer decision-making styles of young-adult Chi-
nese,” Journal of Consumer Affaires, Vol. 32, 1998, pp. 275-289.
6. M. Fishbein, The Relationship Between Beliefs, Attitudes, and Behavior, Cognitive
Consistency, S. Feldman, ed., Academic, New York, 1966, pp. 199-223.
7. G. D. Garson, “Interpreting neural-network connection weights,” AI Expert, 1991,
pp. 47-51.
8. S. Haykin, Neural Networks: A Comprehensive Foundation, MacMillian and IEEE
Computer Society, New York, 1994.
9. K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are
universal approximators,” Neural Networks, Vol. 2, 1989, pp. 359-366.
10. M. Ishikawa, “Structural learning with forgetting,” Neural Networks, Vol. 9, 1996,
pp. 509-521.
11. C. Klimasauskas, “Neural networks: An engineering perspective,” IEEE Communi-
cation Magazine, Vol. 30, 1992, pp. 50-53.
12. R. Lippmann, “An introduction to computing with neural nets,” IEEE ASSP Maga-
zine, Vol. 4, 1987, pp. 4-22.
13. X. Sun and R. Collins, “Attitudes and consumption values of consumers of imported
fruit in Guangzhou, China,” International Journal of Consumer Studies, Vol. 26,
2002, pp. 34-43.
14. X. Sun and R. Collins, “A comparison of attitudes among purchasers of imported
fruit in Guangzhou and Urumqi, China,” Food Quality and Preference, Vol. 15,
2004, pp. 229-237.
15. O. H. M. Yau, Consumer Behavior in China: Customer Satisfaction and Cultural
Values, Routledge, London, 1994.
Jaesoo Kim (金在洙) is an Associate Professor at Depart-
ment of Computer Science and Engineering, Seoul National Uni-
versity of Technology. He received his M.S. and Ph.D. degrees
in Computer Science and Information Science from Monash
University, Australia, University of Otago, New Zealand in 1992
and 1999, respectively, and his B.S. degree in Computer Science
from Seoul National University of Technology, South Korea in
1988. Before joining Seoul National University of Technology
as a Professor, he worked as an Assistant Professor at Zayed
University, UAE from 2002 to 2003, and University of Queen-
JAESOO KIM AND HEEJUNE AHN
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sland, Australia from 2000 to 2001. Before his Ph.D., he worked as a research engineer
at Otago University, New Zealand from 1997 to 1999, University of Auckland, New
Zealand from 1993 to 1996, and Monash University, Australia from 1989 to 1992. His
interests include artificial intelligence, computing intelligence, web based data mining,
and intelligent business decision system.
Heejune Ahn (安熙準) received his Ph.D., M.S., and B.S.
degrees in Electrical Engineering from KAIST (Korea Advanced
Institute of Technology), Daejeon, the Republic of Korea, in 1999,
1995 and 1993, respectively. He is an Assistant Professor of the
Department of Control and Instrumentation Engineering at Seoul
National University of Technology, Seoul, the Republic of Korea.
He worked as a visiting researcher at Telecommunication Lab. of
Erlangen-Nuremberg University, Germany, from July 1999 to
February 2002. He has been a GSM/GPRS/UMTS wireless mo-
bile protocol software engineer at Next Generation Handset Lab.,
LG Electronics Inc., Korea, from February 2000 to September 2002. From September
2002 to December 2003 he worked as a software architect and programmer of J2EE web
server system at Tmax Soft Inc. His research interests include multimedia communica-
tions, protocol development, network system performance analysis, and real-time embed-
ded systems.
