Performance Analysis of Filter Based Feature Selection Techniques in Tree Based Classification Methods
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Department of Computer Science & Information Technology
Abstract
Classification has been called the most influential development in Data Mining and Machine
Learning in the past decade. The idea of classification is to find the class of the unknown
objects based on their attributes.
In this thesis, the performance of decision tree based classification methods is analysed with
feature selection methods; Chi-square and Relief. The feature selection process chooses
optimal subset of features according to objective function. These feature selection method
helps to remove unnecessary attributes from the high dimensional dataset, thus improves the
efficiency of the classification algorithms. The performance of feature section methods; Chisquare
and
Relief
were compared in Tree based classification methods; C4.5, CART, LMT
and Random Tree. The study shows that the Chi-square feature selection method is more
suitable while using with LMT followed by C4.5, Random Tree and CART respectively. In
case Relief based feature selection method LMT gives the best result followed by CART,
C4.5 and Random Tree respectively.