Comprehensible knowledge discovery using particle swarm optimization with monotonicity constraints

Chih Chuan Chen, Chao Chin Hsu, Yi Chung Cheng, Sheng-Tun Li, Ying Fang Chan

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Due to uncertain data quality, knowledge extracted by methods merely focusing on gaining high accuracy might result in contradiction to experts' knowledge or sometimes even common sense. In many application areas of data mining, taking into account the monotonic relations between the response variable and predictor variables could help extracting rules with better comprehensibility. This study incorporates Particle Swarm Optimization (PSO), which is a competitive heuristic technique for solving optimization tasks, with constraints of monotonicity for discovering accurate and comprehensible rules from databases. The results show that the proposed constraints-based PSO classifier can exploit rules with both comprehensibility and justifiability.

Original languageEnglish
Title of host publicationOpportunities and Challenges for Next-Generation Applied Intelligence
EditorsBeen-Chian Chien
Pages323-328
Number of pages6
DOIs
Publication statusPublished - 2009 Dec 1

Publication series

NameStudies in Computational Intelligence
Volume214
ISSN (Print)1860-949X

Fingerprint

Particle swarm optimization (PSO)
Data mining
Classifiers

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Chen, C. C., Hsu, C. C., Cheng, Y. C., Li, S-T., & Chan, Y. F. (2009). Comprehensible knowledge discovery using particle swarm optimization with monotonicity constraints. In B-C. Chien (Ed.), Opportunities and Challenges for Next-Generation Applied Intelligence (pp. 323-328). (Studies in Computational Intelligence; Vol. 214). https://doi.org/10.1007/978-3-540-92814-0_50
Chen, Chih Chuan ; Hsu, Chao Chin ; Cheng, Yi Chung ; Li, Sheng-Tun ; Chan, Ying Fang. / Comprehensible knowledge discovery using particle swarm optimization with monotonicity constraints. Opportunities and Challenges for Next-Generation Applied Intelligence. editor / Been-Chian Chien. 2009. pp. 323-328 (Studies in Computational Intelligence).
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Chen, CC, Hsu, CC, Cheng, YC, Li, S-T & Chan, YF 2009, Comprehensible knowledge discovery using particle swarm optimization with monotonicity constraints. in B-C Chien (ed.), Opportunities and Challenges for Next-Generation Applied Intelligence. Studies in Computational Intelligence, vol. 214, pp. 323-328. https://doi.org/10.1007/978-3-540-92814-0_50

Comprehensible knowledge discovery using particle swarm optimization with monotonicity constraints. / Chen, Chih Chuan; Hsu, Chao Chin; Cheng, Yi Chung; Li, Sheng-Tun; Chan, Ying Fang.

Opportunities and Challenges for Next-Generation Applied Intelligence. ed. / Been-Chian Chien. 2009. p. 323-328 (Studies in Computational Intelligence; Vol. 214).

Research output: Chapter in Book/Report/Conference proceedingChapter

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AB - Due to uncertain data quality, knowledge extracted by methods merely focusing on gaining high accuracy might result in contradiction to experts' knowledge or sometimes even common sense. In many application areas of data mining, taking into account the monotonic relations between the response variable and predictor variables could help extracting rules with better comprehensibility. This study incorporates Particle Swarm Optimization (PSO), which is a competitive heuristic technique for solving optimization tasks, with constraints of monotonicity for discovering accurate and comprehensible rules from databases. The results show that the proposed constraints-based PSO classifier can exploit rules with both comprehensibility and justifiability.

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Chen CC, Hsu CC, Cheng YC, Li S-T, Chan YF. Comprehensible knowledge discovery using particle swarm optimization with monotonicity constraints. In Chien B-C, editor, Opportunities and Challenges for Next-Generation Applied Intelligence. 2009. p. 323-328. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-540-92814-0_50