UNBIASEDNESS OF FEATURE SELECTION BY HYBRID FILTERING
DOI:
https://doi.org/10.47839/ijc.10.1.735Keywords:
Feature selection, hybrid algorithms, machine learning, classification.Abstract
In this article we examine characteristics of feature selection algorithms by introducing their aspects important in practice. We will focus on the unbiasedness, analyse it and investigate a robust hybrid method of feature selection, being a composition of several feature filters, that could ensure unbiased results of selection. Using parallel multi-measures and voting, we reduce the risk of selecting non-optimal features, a common situation when we select attributes using single evaluation based on one evaluation criterion. To test this method we selected a personal bankruptcy dataset, containing various types of attributes and one of the popular machine learning benchmarks. By the performed experiments we will demonstrate that an approach of multi-evaluation used for features filtering may lead to the creation of effective and fast methods of features selection with an unbiased outcome.References
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