A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization

Associative classification is recognized by its high accuracy and strong flexibility in managing unstructured data. However, the performance is still induced by low quality dataset which comprises of noised and distorted data during data collection. The noisy data affected support value of an itemse...

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Main Authors: Chern-Tong, H., Aziz, I.A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.21914 /
Published: Springer Verlag 2018
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029598698&doi=10.1007%2f978-3-319-67621-0_18&partnerID=40&md5=68d8481b92f9b67593940bb6869e206e
http://eprints.utp.edu.my/21914/
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spelling utp-eprints.219142018-08-01T01:13:11Z A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization Chern-Tong, H. Aziz, I.A. Associative classification is recognized by its high accuracy and strong flexibility in managing unstructured data. However, the performance is still induced by low quality dataset which comprises of noised and distorted data during data collection. The noisy data affected support value of an itemset and so it influenced the performance of an associative classification. The performance of associative classification is relied on the classification where the classification is worked based on the class association rules which generated from frequent rule mining process. To optimize the frequent itemsets based on the support value, in this research, we proposed a new optimization pruning technique to prune decision tree according to the correlation of each decision tree branches using genetic algorithm. © 2018, Springer International Publishing AG. Springer Verlag 2018 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029598698&doi=10.1007%2f978-3-319-67621-0_18&partnerID=40&md5=68d8481b92f9b67593940bb6869e206e Chern-Tong, H. and Aziz, I.A. (2018) A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization. Advances in Intelligent Systems and Computing, 662 . pp. 195-203. http://eprints.utp.edu.my/21914/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Associative classification is recognized by its high accuracy and strong flexibility in managing unstructured data. However, the performance is still induced by low quality dataset which comprises of noised and distorted data during data collection. The noisy data affected support value of an itemset and so it influenced the performance of an associative classification. The performance of associative classification is relied on the classification where the classification is worked based on the class association rules which generated from frequent rule mining process. To optimize the frequent itemsets based on the support value, in this research, we proposed a new optimization pruning technique to prune decision tree according to the correlation of each decision tree branches using genetic algorithm. © 2018, Springer International Publishing AG.
format Article
author Chern-Tong, H.
Aziz, I.A.
spellingShingle Chern-Tong, H.
Aziz, I.A.
A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization
author_sort Chern-Tong, H.
title A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization
title_short A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization
title_full A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization
title_fullStr A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization
title_full_unstemmed A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization
title_sort performance evaluation of chi-square pruning techniques in class association rules optimization
publisher Springer Verlag
publishDate 2018
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029598698&doi=10.1007%2f978-3-319-67621-0_18&partnerID=40&md5=68d8481b92f9b67593940bb6869e206e
http://eprints.utp.edu.my/21914/
_version_ 1741196541316562944
score 11.62408

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