A Comprehensive Feature Selection Approach for Machine Learning

Abstract

In machine learning, it is required that the underlying important input variables are known or else the value of the predicted outcome variable would never match the value of the target outcome variable. Machine learning tools are used in many applications where the underlying scientific model is inadequate. Unfortunately, making any kind of mathematical relationship is difficult, and as a result, incorporation of variables during the training becomes a big issue as it affects the accuracy of results. Another important issue is to find the cause behind the phenomena and the major factor that affects the outcome variable. The aim of this article is to focus on developing an approach that is not particular-tool specific, but it gives accurate results under all circumstances. This paper proposes a model that filters out the irrelevant variables irrespective of the type of dataset that the researcher can use. This approach provides parameters for determining the quality of the data used for mining purposes.