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


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
Number of pages6
Publication statusPublished - 2009

Publication series

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

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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