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As the interest in machine learning and data mining springs up, the problem of how to assess learning algorithms and compare classifiers become more pressing. This has been associated with the lack of comprehensive and complete workflow depending on the project scale to provide guidance to its users. This means the success or failure of the project...
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Context 1
... of the data are grouped together depending on the similarity of the characteristics in the clustering system that tries to identify and extract similar groups of observation from the dataset (Raynor 1999;Han & Kamber 2002). Figure 4 represents clusters for the five musical classes; hip hop, pop, punk, electronica and netal. ...
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... are assessed depending on performance measure(s); accuracy, error rate, precision, recall and ROC curve. In SAS Enterprise Miner the comparison is done by the model comparison icon as shown in figure 4 where decision tree, neural network and regression techniques are compared. These three workflows, CRISP-DM, KDD and SAS-SEMMA are mostly used for large machine learning and data mining projects. ...
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... algorithm parameters, the experimenter is expected to be ready to perform the experiment and hence the next step is to shift to the outer layer of the workflow where there are two steps; experimentation and evaluation. Figure 24 presents the proposed classifier workflow. The arrows in the classifier workflow in figure 22 indicate the starting and finishing points of the phases and the dependencies between them. ...
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If you want to tell people the truth, make them laugh, otherwise they'll kill you. (source unclear) Machine learning and deep learning are the technologies of the day for developing intelligent automatic systems. However, a key hurdle for progress in the field is the literature itself: we often encounter papers that report results that are difficul...
Citations
... This technique is unique compared with the earlier-mentioned techniques in that it only works by storing the training data provided. When a new query or instance is started, an identical set of related instances or neighbors is retrieved from the memory and used to classify the new instance [33]. ...
... This technique is unique compared with the earlier-mentioned techniques in that it only works by storing the training data provided. When a new query or instance is started, an identical set of related instances or neighbors is retrieved from the memory and used to classify the new instance [33]. ...
Brain tumors are life-threatening for adults and children. However, accurate and timely detection can save lives. This study focuses on three different types of brain tumors: Glioma, meningioma, and pituitary tumors. Many studies describe the analysis and classification of brain tumors, but few have looked at the problem of feature engineering. Methods are needed to overcome the drawbacks of manual diagnosis and conventional feature-engineering techniques. An automatic diagnostic system is thus necessary to extract features and classify brain tumors accurately. While progress continues to be made, the automatic diagnoses of brain tumors still face challenges of low accuracy and high false-positive results. The model presented in this study, which offers improvements for feature extraction and classification, uses deep learning and machine learning for the assessment of brain tumors. Deep learning is used for feature extraction and encompasses the application of different models such as fine-tuned Inception-v3 and fine-tuned Xception. The classification of brain tumors is explored through deep- and machine-learning algorithms including softmax, Random Forest, Support Vector Machine, K-Nearest Neighbors, and the ensemble technique. The results of these approaches are compared with existing methods. The Inception-v3 model has a test accuracy of 94.34% and achieves the highest performance compared with other recently reported methods. This improvement may be sufficient to support a significant role in clinical applications for brain tumor analysis. Furthermore, this type of approach can be used as an effective decision-support tool for radiologists in medical diagnostics as a second opinion based on the magnetic resonance imaging (MRI) analysis. It may also save valuable time for radiologists who have to manually review numerous MRI images of patients.
