SemiSupervised Learning for Class Association Rule Mining Using Genetic Network Programming

Data mining extracts useful knowledge from big data. The extracted knowledge in data mining is often represented by association rules, and association rules can be also used for classification. However, when association rules for classification (called class association rules) are extracted, a large...

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Bibliographic Details
Published in:IEEJ transactions on electrical and electronic engineering Vol. 15; no. 5; pp. 733 - 740
Main Authors: Mabu, Shingo, Higuchi, Takuro, Kuremoto, Takashi
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 01.05.2020
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Subjects:
Annotations
class association rule
Classification
Classifiers
Data mining
Evolutionary algorithms
genetic network programming
Semi-supervised learning
semisupervised learning
Supervised learning
ISSN:1931-4973, 1931-4981
Online Access:Get full text
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Summary:Data mining extracts useful knowledge from big data. The extracted knowledge in data mining is often represented by association rules, and association rules can be also used for classification. However, when association rules for classification (called class association rules) are extracted, a large number of data with class labels are necessary, which requires a lot of cost of manual annotation. Therefore, this paper proposes a semisupervised learning method for rule extraction, where a small number of labeled data and a large number of unlabeled data are used to efficiently extract class association rules. In detail, this paper proposes two types of classifiers using class association rules: one is a classifier based on semisupervised learning, and the other is that based on both supervised and semisupervised learning. The second method builds several classifiers using supervised learning and semisupervised learning and the classification results of these classifiers are integrated to make the final decision. As an association rule mining method, Genetic Network Programming (GNP), which is one of the graph‐based evolutionary algorithms is used. GNP has shown distinguished classification ability in some applications; therefore, one of the other objectives of this paper is to extend the applicability of GNP to data mining based on semisupervised learning. In the experiments, the classification accuracy is evaluated using some benchmark datasets by comparing with some conventional methods. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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ISSN:1931-4973
1931-4981
DOI:10.1002/tee.23109