An Empirical Study on Different Ranking Methods for Effective Data Classification

Authors

Ilangovan Sangaiah, K.L.N. College of Engineering, Madurai, IndiaFollow
A. Vincent Antony Kumar, PSNA College of Engineering and Technology, Dindigul, IndiaFollow
Appavu Balamurugan, K.L.N. College of Information Technology, Tamil Nadu, IndiaFollow

Abstract

Ranking is the attribute selection technique used in the pre-processing phase to emphasize the most relevant attributes which allow models of classification simpler and easy to understand. It is a very important and a central task for information retrieval, such as web search engines, recommendation systems, and advertisement systems. A comparison between eight ranking methods was conducted. Ten different learning algorithms (NaiveBayes, J48, SMO, JRIP, Decision table, RandomForest, Multilayerperceptron, Kstar) were used to test the accuracy. The ranking methods with different supervised learning algorithms give different results for balanced accuracy. It was shown the selection of ranking methods could be important for classification accuracy.

DOI

10.22237/jmasm/1446350760