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

研究成果: Chapter

摘要

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.

原文English
主出版物標題Opportunities and Challenges for Next-Generation Applied Intelligence
編輯Been-Chian Chien
頁面323-328
頁數6
DOIs
出版狀態Published - 2009 十二月 1

出版系列

名字Studies in Computational Intelligence
214
ISSN(列印)1860-949X

    指紋

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

引用此

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. 於 B-C. Chien (編輯), Opportunities and Challenges for Next-Generation Applied Intelligence (頁 323-328). (Studies in Computational Intelligence; 卷 214). https://doi.org/10.1007/978-3-540-92814-0_50