Rule based Hybrid Weighted Fuzzy Classifier for Tumor Data

Abstract

Examination of gene based information has turned out to be so essential in biomedical industry for assurance of basic ailments. A fuzzy rule based classification is a standout amongst the most mainstream approaches utilized as a part of example arrangement issues. The fuzzy rule based classifier creates an arrangement of fuzzy if-then decides that empower exact non-straight order of information designs. In spite of the fact that there are different techniques to create fluffy if-then guidelines, the advancement of lead producing process is as yet an issue. Here, we introduce a half and half weighted fluffy order framework in which few fluffy if-then principles are chosen by methods for offering weights to preparing designs. Further, we utilize a genetic algorithm (GA) to streamline the classifier for quality articulation investigation

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International Journal of Engineering & Technology, 7 (4.19) (2018) 104-108
International Journal of Engineering & Technology
Website: www.sciencepubco.com/index.php/IJET
Research paper
Rule based Hybrid Weighted Fuzzy Classifier for Tumor Data
D. Winston Paul1*, S. Balakrishnan2, A.Velusamy3
1, 2, 3 Department of Information Technology,
Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, India
*Corresponding Author Email: 1winstonpauld@skcet.ac.in
Abstract
Examination of gene based information has turned out to be so essential in biomedical industry for assurance of basic ailments. A fuzzy
rule based classification is a standout amongst the most mainstream approaches utilized as a part of example arrangement issues. The
fuzzy rule based classifier creates an arrangement of fuzzy if-then decides that empower exact non-straight order of information designs.
In spite of the fact that there are different techniques to create fluffy if-then guidelines, the advancement of lead producing process is as
yet an issue. Here, we introduce a half and half weighted fluffy order framework in which few fluffy if-then principles are chosen by
methods for offering weights to preparing designs. Further, we utilize a genetic algorithm (GA) to streamline the classifier for quality
articulation investigation
Keywords: Data mining, classificaton, Bioinformatics, Fuzzy sytems ,genetic algorithms, weighted rule.
1. Introduction
In the process of DNA analysis, microarray studies have received
a lot of attention, industrially as well as in research. The gene
expression analysis plays an inevitable role of both identification
and prediction of DNA sequences. Microarray trials can either
screen every quality a few times under fluctuating conditions or
examine the qualities in a solitary situation however in various
sorts of tissue [1], [2].
Here, we center around the arrangement of quality articulations.
“Information mining has pulled in a lot of consideration in the
data business and in the public arena in general as of late, because
of the wide accessibility of gigantic measures of information and
the up and coming requirement for transforming such information
into helpful data and learning [5]”. Arrangement is an undertaking
that happens much of the time in regular day to day existence.
Basically it includes separating up objects with the goal that each
is relegated to one of various commonly thorough and select
classifications known as classes [6].
In this paper, we utilize mixture weighted fluffy manage based
characterization framework for dissecting microarray articulation
information. Fluffy frameworks in view of fluffy if-then principles
have been connected to different control issues. One favorable
position of such fluffy framework is their interpretability. As of
late fluffy manage based framework have likewise been connected
to design characterization issues [4]. These are numerous ways to
deal with consequently create fluffy if-then guidelines from
numerical information for design grouping issues, for example,
Michigan, Pittsburgh or Iterative lead learning approaches. In the
proposed work, we enhance the Michigan based fuzzy rule
scheme by adding weights for training patterns. Thus we can
reduce the number of rules generated exponentially there by
reducing number of attributes involved. Further, using genetic
algorithm (GA) [11] we can make “a compact classifier for gene
expression analysis”.
2. Background
2.1. Fuzzy Rule Based Classification
Pattern classification normally is a directed procedure where, in
light of known information, anticipate choice for new information.
The known information is called preparing set and the new
information is called test information. Different techniques have
been proposed for fluffy order. “Let us assume that our pattern
classification problem is an n-dimensional problem with C classes
and m given training patterns”
Yp= (yp1, yp2, …, ypm), p=1, 2, …, m.
“Without loss of generality, each attribute of the given training
patterns is normalized into a unit interval [0, 1]; That is, the
pattern space is an n-dimensional unit hypercube [0, 1]n”. In this
study we use “fuzzy if-then rules of the following type as a base of
our fuzzy rule-based classification systems:
Rule Rj: If y1 is Aj1 and ... and yn is Ajn then
Class Cj with CFj, j=1, 2, …, N
where Rj is the label of the jth fuzzy if-then rule, Aj1, … ,Ajn are
antecedent fuzzy sets on the unit interval [0, 1], Cj is a consequent
class (i.e. one of the given classes), CFj is the grade of certainty of
the fuzzy if-then rule Rj, and N is the total number of fuzzy if-then
rules”. As “antecedent fuzzy sets, we use triangular fuzzy sets as
in Fig.1and Fig.2 where we show various partitions of the unit
interval into a number of fuzzy sets” [3].
The “Fig.1 shows triangular membership function and Fig.2
Shows the triangular membership function based on five fuzzy
set”. So, it contains five classes and 25 rules are generated (if the
number of attribute is 2) for each attribute.

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Fig. 1: Triangular membership Function
Fig. 2: Five Fuzzy Set Membership Function
2.2. Fuzzy if-then Rule Generation
Step 1: Calculate βclass h (Rj) for Class h as
Where, µj(yp) = µj1(yp1).…. µjn(ypm).
P=1, 2, …, m. h=1,2,…,C.
Step 2: Find Class ĥ that has the maximum value of βclass h (Rj),
where βclass h (Rj) is the “average of the compatibility grades of
training patterns in Class h with the fuzzy if-then rule Rj and
Nclass h is the number of training patterns which their
corresponding class is Class h and each of the fuzzy rules in the
final classification has a certainty grade, which denotes the
strength of that fuzzy rule”. This number is calculated according
to,
Where,
2.3. Fuzzy Rule Mapping
“When a rule set S is given, an input pattern Y=(y1,y2,…,yn) is
classified by a single winner rule Rj in S, which is determined as
follow:
That is the “winner rule has the maximum product of
compatibility and the certainty grade CFj”. The “classification is
rejected if no fuzzy if-then rule is compatible with the input
pattern Yp”. The generation of each fuzzy rule is accepted only if
its consequent class is the same as its corresponding random
pattern class. Otherwise, the generated fuzzy rule is rejected.
3. Related Work
There are several approaches for classifying gene expression data.
In [1], the clinical research approach method is used to categorize
different types of cancer tissues and to identify new cancer
classes. The approach [1] “used here is based on gene expression
monitoring by DNA microarrays and are applied to human
leukemia’s as test case”. The gene expression databases are pillars
of the research [2] and mining from these databases is a major
task.
In section II, we have explained the generic fuzzy classification
system as expressed in [3], [7]. An “n-dimensional classification
problem is analyzed using a hybrid fuzzy method”. The hybrid
fuzzy classification system is formed by combining the fuzzy
classification scheme and genetic algorithm [10]-[12]. The “fuzzy
classification scheme is used to generate the fuzzy if-then rules,
which increases the number of rules exponentially with the
number of attributes involved”.
In order to reduce the rule generation, the modern genetic
algorithm is used. Some other methods have also been applied
using rule weights and weights for training patterns to reduce the
rules by avoiding unimportant attributes [7], [8]. Applying
weights for training patterns prove to be better than rule weights
[7]. However, optimization is not achieved completely.
The main contribution of this paper is hybridizing the evolutionary
genetic algorithm with weighted training patterns for optimizing
the rule generation. As a result, the searching complexity of rule
mapping process is reduced.
4. Hybrid Weighted Fuzzy Method
In our fuzzy rule generation approach, the weight is given for each
training pattern. The initial population is generated from the
weighted training patterns and is subjected to fitness evaluation.
To achieve more compactness for classifier, the current population
is further subjected to genetic operations.
4.1. Weights for Training Patterns
The motivation behind weight is to give need for all preparation
designs. The heaviness of preparing examples can be seen as a
critical factor of the examples. There are different techniques
accessible for weight task, for example, class-based weighting
strategy, cover based weighting technique and arbitrary weighting
technique and so on. Here we underline on class based weighting
technique.
The class-based weighting technique is to make an inclination
toward the grouping of examples from a specific class. For
instance, if the predisposition is toward the arrangement of Class1
designs, characterization frameworks are relied upon to accurately
order Class 1 designs regardless of whether the quantity of
misclassification/dismissal is substantial for different classes.
In this “weighting method, a weight for the pattern Yp is
determined by following equation”:
4.2. Rules from Weighted Training Patterns
Let us assume that we have m training patterns Yp= (yp1,yp2, …,
ypm), p=1,2, …,m. Then the rule procedure is,
[Step 1]: Calculate βclass h (Rj) for Class h as
h=1,2, …. C

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International Journal of Engineering & Technology
where, µj(yp) = µj1(yp1).…. µjn(ypm).
[Step 2]: Find Class ĥ that has the maximum value of βclass h
(Rj),
The remaining procedures are same as explained earlier.
4.3. Architecture
Fig. 3: Hybrid weighted fuzzy classifier
The Fig.3 shows the overall structure of the hybrid weighted fuzzy
if-then rule based classification system. The known classified data
is taken as input for training process. The weight is multiplied
with each training pattern and the summation is given for internal
input for generating rules. The rules are stored in rule pool and
each rule is submitted to fitness evaluation and then for genetic
operation, to remove worst rules. Finally, the unknown test data is
given for classification.
4.4. Genetic Algorithm
Genetic algorithms are roused by Darwin's hypothesis of
development. It is a cutting edge calculation for taking care of any
enhancement issue. Answer for an issue illuminated by hereditary
calculations utilizes a developmental procedure. Calculation
begins with arbitrarily producing the arrangement of conceivable
answers for an advancement issue called populace. Arrangements
from one populace are taken and used to shape another populace.
This is inspired by an expectation, that the new populace will be
superior to the old one. At that point arrangements, which are
chosen to frame new arrangements, are chosen by their wellness -
the more appropriate they are the more shots they need to imitate.
This is rehashed until the point when some condition is fulfilled.
The hereditary tasks are determination, hybrid and transformation.
The new guidelines are created from the principles in the present
populace utilizing hereditary activities. To perform hybrid task,
two fluffy if-then standards are arbitrarily chosen from the present
populace and the better administer with the higher wellness
esteem is picked as a parent string. A couple of parent strings is
picked by repeating this strategy twice. From the chose match of
parent strings, two new strings are produced by a hybrid activity.
The “crossover operator is applied to each pair of parent strings
with a pre-specified crossover probability pc”.
After new strings are generated, the mutation operation will be
taken for replacing the current string with the pre-specified
mutation probability pm.
4.5. Crossover Operation
Two parents deliver two posterity. Quite possibly the
chromosomes of the two guardians are replicated unmodified as
posterity. Quite possibly the chromosomes of the two guardians
are arbitrarily recombined (hybrid) to frame posterity. For the
most part the possibility of hybrid is in the vicinity of 0.6 and 1.0.
Fig. 4: Example of Crossover
The Fig.4 shows the single point crossover operation. The third bit
of the parent strings is crossover to produce new child strings. The
main goal of the cross over is to decompose two distinct solutions
and then randomly mixes their parts to form novel solutions.
4.6. Mutation Operation
There is a chance that a gene of a child is changed randomly.
Generally the chance of mutation is low (e.g. 0.001).
Fig 5: Example of Mutation
In Fig.5 the original binary string is mutated by switching few bits
from 1 to 0 or from 0 to 1. The “selection, crossover and mutation
are iterated until a pre-specified number Nreplace of new strings
are generated”.
4.7. Algorithm Summary
The Fig.6 describes the flow of processes involved in the
classification system. Initially, a set of rules are generated from
the weighted training patterns and then an iterative process is
taken for checking fitness and genetic operations until the
stopping condition is satisfied.
Fig. 6: Flow Chart

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The steps involved in hybrid weighted fuzzy rule based classifier
are following:
Step 1: “Parameter Specification : Specify the number of fuzzy if-
then rules Nrule, the number of replaced rules Nreplace, the
crossover probability pc, the mutation probability pm, weights for
training patterns wp, and the stopping condition
Step 2: Weight assignment: Assign weights (wp) for each training
pattern
Step 3: Initialize: Randomly generate Nrules from weighted
training patterns as an initial population
Step 4: Check Fitness: Evaluate the fitness of each rule in the
current population, and test if the end condition is satisfied, stop,
return the best solution, and exit
Step 5: Genetic Operations: Create a new population based on
current population by following genetic operations
Selection: Select two rules randomly from the current population
according to their fitness (the better fitness, the bigger chance to
be selected)
4.8. Cross Over
With a crossover probability cross over the selected parent rules to
form new offspring (children/rules). If no crossover was
performed, offspring is the exact copy of parents
4.9. Mutation
With a mutation probability mutate new offspring at each locus
Step 6: Replace: Place current population using new generated
offspring population for a further run of the algorithm
Step 7: Repeat: Go to step 2”.
4.10. Fitness Evolution
The fitness value of each run is assessed by grouping all the given
preparing designs utilizing the arrangement of fluffy if-then
standards in the present populace. The wellness estimation of the
fluffy if-then control is assessed by the accompanying wellness
work:
fitness (Rj) = NCP(Rj) – wp . NMP(Rj)
where, “NCP(Rj) denotes the number of correctly classified
training patterns by rules Rj and NMP(Rj) is the number of
misclassified training patterns”.
4.11. Cost and Accuracy Evaluation
The cost of misclassification or rejection of fuzzy classification
system is calculated as,
Where, “Cost(S) is the cost of misclassification or rejection made
by a fuzzy classification system S, m is the number of training
patterns, wp is the weight of the training pattern yp, and zp(S) is a
binary variable that is determined according to the classification
result of the training pattern xp by S: zp(S) = 0 if yp is correctly
classified by S and zp(S)=1 otherwise[7]”.
The accuracy of the hybrid weighted fuzzy rule based classifier is
calculated as,
Accuracy, a=t/n
Where,
t-> number of samples correctly classified.
n-> total number of sample cases.
5. Experimental Results
We have developed a hybrid weighted fuzzy rule based system
trained by genetic algorithm. The colon cancer and breast cancer
datasets are taken for experimental process. The number of classes
used is 2. The colon dataset contain 6 attributes and 400 patterns.
The breast cancer dataset contains 523 patters and 9 attributes. For
result, we have used the yes or no classes. It is clear that the
method with weights for training patterns can obtain better
classification and fewer rules. The table 1 shows the parameters of
genetic algorithm used for the hybrid weighted fuzzy classifier.
Table 1: Parameter Specification
Parameter
Value
Number of rules, Nrule
20
Crossover probability, Pc
0.9
Mutation probability, Pm
0.1
Number of replaced rules, Nreplace
4
Stopping Condition Cycle
50
6. Conclusion
In this paper, a hybrid weighted fuzzy rule based classification
system is developed with the combination of assigning weights for
training patterns, fuzzy rule generation procedure and genetic
algorithm. The Michigan based procedure is used to construct the
structure. The compactness of classifier also good compared with
existing system. The experimental result shows the hybrid
weighted fuzzy rule based classifier gives higher performance than
the conventional methods.
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