C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Support Vector Data Description
Machine Learning
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Expert Systems with Applications: An International Journal
Face detection using kernel PCA and imbalanced SVM
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Density-Induced Support Vector Data Description
IEEE Transactions on Neural Networks
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Data description and classification are important tasks in supervised learning. In this study, three supervised learning methods such as k-nearest neighbour k-NN, support vector data description SVDD and support vector machine SVM are considered because they do not suffer from the problem of introducing a new class. The data sample chosen is Pima Indians diabetes. The results show that feature selection based on mean information gain and a standard deviation threshold can be considered as a substitute for forward selection. This indicates that data variation using information gain is an important factor that must be considered in selecting feature subset. Finally, among eight candidate features, glucose level is the most prominent feature for diabetes detection in all classifiers and feature selection methods under consideration. Relevancy measurement in information gain can sort out the most important feature to the least significant one. It can be very useful in medical applications such as defining feature prioritisation for symptom recognition.