Feature Selection Algorithms in Medical Data Classification: A Brief Survey and Experimentation

摘要

Feature selection algorithms play a crucial role in any machine learning problem. Choice of the best algorithm yields optimal subset of features thereby increasing the accuracy and reducing the time required for training. In the case of high dimensional datasets it is also advantageous in removing the irrelevant features. This paper presents a novel approach of surveying the popular feature selection algorithms specifically used in medical data classification, by considering the following types of medical data—signals, images and numerical. This work shall be very useful to researchers in collecting first hand information since we have reviewed the various aspects such as—available medical datasets, feature selection techniques, choice of classifier, issues in identifying the feature selection technique, analysis of major feature selection methodologies and detailed mechanisms thereof. We have also performed sample experimentation on the standard medical datasets from UCI and analyzed the effects on time and performance by employing 12 popular classifiers. The results demonstrate improved accuracy and lowered computation times.