An Evaluation of Feature Selection Methods for Multiclass Learning in Bio Informatics

Traditional data mining techniques such as classification or clustering have demonstrated achievement in datasets which has multiple instances in singly relation but while extreme point of dimensionality or complex dependencies presents in the data it fails to offer accuracy and correctness. In solution to this, Feature (attribute/variable) selection techniques since last two decades have verified its requisites to improve speed, prediction and reduce computational cost of machine learners. In this paper review of assorted feature selection methods named filter, wrapper and embedded with each classifier like support vector machines (SVM), averaged perceptron and neural network is presented. Additionally it conveys an assessment of which FS approach works better for which classifier for breast cancer dataset.