CHISC-AC: Compact Highest Subset Confidence-Based Associative Classification
The associative classification method integrates association rule mining and classification. Constructing an efficient classifier with a small set of high quality rules is a highly important but indeed a challenging task. The lazy learning associative classification method successfully removes the n...
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doaj-69a9ea5a22674d1d9c6cc947f6f80e0f2020-11-25T00:30:58ZengUbiquity PressData Science Journal1683-14702014-11-011312713710.2481/dsj.14-03510CHISC-AC: Compact Highest Subset Confidence-Based Associative ClassificationS P Syed Ibrahim0K R Chandran1C J Kabila Kanthasam2School of Computing Science and Engineering, VIT University - Chennai Campus, Tamilnadu, IndiaDepartment of Information Technology, PSG College of Technology, Coimbatore, Tamilnadu, IndiaDepartment of Information Technology, Avinashilingam University for Women, Coimbatore, Tamilnadu, IndiaThe associative classification method integrates association rule mining and classification. Constructing an efficient classifier with a small set of high quality rules is a highly important but indeed a challenging task. The lazy learning associative classification method successfully removes the need for a classifier but suffers from high computation costs. This paper proposes a Compact Highest Subset Confidence-Based Associative Classification scheme that generates compact subsets based on information gain and classifies the new samples without constructing classifiers. Experimental results show that the proposed system out performs both the traditional and the existing lazy learning associative classification methods. −−−−− Paper presented at 1st International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2014) March 27-28, 2014. Organized by VIT University, Chennai, India. Sponsored by BRNS.http://datascience.codata.org/articles/10Associative classificationLazy learningInformation gainConfidence |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
S P Syed Ibrahim K R Chandran C J Kabila Kanthasam |
spellingShingle |
S P Syed Ibrahim K R Chandran C J Kabila Kanthasam CHISC-AC: Compact Highest Subset Confidence-Based Associative Classification Data Science Journal Associative classification Lazy learning Information gain Confidence |
author_facet |
S P Syed Ibrahim K R Chandran C J Kabila Kanthasam |
author_sort |
S P Syed Ibrahim |
title |
CHISC-AC: Compact Highest Subset Confidence-Based Associative Classification |
title_short |
CHISC-AC: Compact Highest Subset Confidence-Based Associative Classification |
title_full |
CHISC-AC: Compact Highest Subset Confidence-Based Associative Classification |
title_fullStr |
CHISC-AC: Compact Highest Subset Confidence-Based Associative Classification |
title_full_unstemmed |
CHISC-AC: Compact Highest Subset Confidence-Based Associative Classification |
title_sort |
chisc-ac: compact highest subset confidence-based associative classification |
publisher |
Ubiquity Press |
series |
Data Science Journal |
issn |
1683-1470 |
publishDate |
2014-11-01 |
description |
The associative classification method integrates association rule mining and classification. Constructing an efficient classifier with a small set of high quality rules is a highly important but indeed a challenging task. The lazy learning associative classification method successfully removes the need for a classifier but suffers from high computation costs. This paper proposes a Compact Highest Subset Confidence-Based Associative Classification scheme that generates compact subsets based on information gain and classifies the new samples without constructing classifiers. Experimental results show that the proposed system out performs both the traditional and the existing lazy learning associative classification methods. −−−−− Paper presented at 1st International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2014) March 27-28, 2014. Organized by VIT University, Chennai, India. Sponsored by BRNS. |
topic |
Associative classification Lazy learning Information gain Confidence |
url |
http://datascience.codata.org/articles/10 |
work_keys_str_mv |
AT spsyedibrahim chiscaccompacthighestsubsetconfidencebasedassociativeclassification AT krchandran chiscaccompacthighestsubsetconfidencebasedassociativeclassification AT cjkabilakanthasam chiscaccompacthighestsubsetconfidencebasedassociativeclassification |
_version_ |
1725324564730216448 |