Feature selection algorithms look to effectively and efficiently find an optimal subset of relevant features
in the data. This pre processing step to machine learning is becoming crucial as the number of features and
data set sizes increases. In this article we will present the unique automatic process we developed at
TapReason for evaluating the effectiveness of different feature selection algorithms for a specific dataset.
In the first and second sections we explain the motivation for performing feature selection and review
common feature selection methods (FSM). The third section describes the method we used in our research
and its results. In the fourth section we present the TapReason algorithm for evaluating and selecting
feature selection algorithms.