Correlation Coefficient Based Candidate Feature Selection Framework Using Graph Construction

Year 2018, Volume: 31 Issue: 3, 775 - 787, 01.09.2018

Sai Prasad Potharaju , Marriboyina Sreedevı

Abstract

Selection
of strong features is crucial problem in machine learning. It is also
considered as an inescapable exercise to minimize the number of
variables available in the primary feature space for finer
classification performance, decrease computation complexity , and
minimized memory utilization. In this current work, a novel structure
using Symmetrical Uncertainty (SU) and Correlation Coefficient (CCE)
by constructing the graph to select the candidate feature set is
presented. The nominated features are assembled into limited number
of clusters by evaluating their CCE and considering the highest SU
score feature. In every cluster, a feature with highest SU score is
selected while remaining features in the same cluster are
disregarded. The presented methodology was investigated with Ten(10)
well known data sets. Exploratory results assures that the presented
method is out pass than most of the traditional feature selection
methods in accuracy. This framework is assessed using Lazy, Tree
Based, Naive Bayes, and Rule Based learners.

Keywords

Feature Selection, Correlation Coefficient, Classification, Machine Learning, Symmetrical Uncertainty

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