Associative Classification With Artificial Immune System

Associative classification (AC), which is based on association rules, has shown great promise over many other classification techniques. To implement AC effectively, we need to tackle the problems on the very large search space of candidate rules during the rule discovery process and incorporate the...

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Vydané v:IEEE transactions on evolutionary computation Ročník 13; číslo 2; s. 217 - 228
Hlavní autori: Tien Dung Do, Siu Cheung Hui, Fong, A.C.M., Fong, B.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.04.2009
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Applied sciences
Artificial immune system (AIS)
Artificial immune systems
Artificial intelligence
Association rules
Associative
associative classification (AC)
Classification
Computer science; control theory; systems
Data mining
Data processing. List processing. Character string processing
Dealing
Evolutionary
Evolutionary computation
evolutionary computations
Exact sciences and technology
Feature extraction
Immune system
Immune systems
Information processing
Learning and adaptive systems
Memory organisation. Data processing
Mining
Pattern recognition
Performance evaluation
Searching
Software
Space technology
Evolutionary algorithm
Expert system
Very large databases
Statistical association
Knowledge base
Data mining
associative classification (AC)
Classification
evolutionary computations
Knowledge discovery
Artificial intelligence
Artiflcial immune system (AIS)
Immune system
ISSN:1089-778X, 1941-0026
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Shrnutí:Associative classification (AC), which is based on association rules, has shown great promise over many other classification techniques. To implement AC effectively, we need to tackle the problems on the very large search space of candidate rules during the rule discovery process and incorporate the discovered association rules into the classification process. This paper proposes a new approach that we call artificial immune system-associative classification (AIS-AC), which is based on AIS, for mining association rules effectively for classification. Instead of massively searching for all possible association rules, AIS-AC will only find a subset of association rules that are suitable for effective AC in an evolutionary manner. In this paper, we also evaluate the performance of the proposed AIS-AC approach for AC based on large datasets. The performance results have shown that the proposed approach is efficient in dealing with the problem on the complexity of the rule search space, and at the same time, good classification accuracy has been achieved. This is especially important for mining association rules from large datasets in which the search space of rules is huge.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2008.923394