Association Based Classification Using Chi-Square Independence Test

Author: Yu-Mei Chang (張毓美)

Publish Year: 2002-07

Update by: March 30, 2025

摘要

For many years, classification s one of the key problems in machine learning research. Since association rule mining is an important and highly active data mining research, there are more and more classification methods based on association rule mining techniques. In this thesis, we study several association based classification methods and provide the comparison of these classifiers. We present a new method, called ACC (i.e. Association based Classification using Chi-square Independence test), to solve the problems of classification.ACC finds frequent and interesting itemsets, which describe the relations between attributes. Moreover, it applies chi-square independence test to remain class-related itemsets for predicting new data objects. Besides, ACC provides an approach that considers the probability of missing value occurrence to solve the problem of missing value. Our method is experimented on 13 datasets from UCI machine learning database repository. We compare ACC with NB and LB, the state-of-the-art classifiers and the experimental results show that our method is a highly effective, accurate classifier.