標題: Integrating extended classifier system and knowledge extraction model for financial investment prediction: An empirical study
作者: Chen, AP
Chen, MY
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: learning classifier system;extended classifier system;knowledge extraction;machine learning
公開日期: 1-七月-2006
摘要: Machine learning methods such as fuzzy logic, neural networks and decision tree induction have been applied to learn rules, however they can get trapped into a local optimal. Based on the principle of natural evolution and global searching, a genetic algorithm is promising for obtaining better results. This article adopts the learning classifier systems (LCS) technique to provide a three-phase knowledge extraction methodology, which makes continues and instant learning while integrates multple rule sets into a centralized knowledge base. This paper makes three important contributions: (1) it represents various rule sets that are derived from different sources and encoded as a fixed-length bit string in the knowledge encoding phase; (2) it uses three criteria (accuracy, coverage, and fitness) to select an optimal set of rules from a large population in the knowledge extraction phase; (3) it applies genetic operations to generate optimal rule sets in the knowledge integration phase. The experiments prove that the rule sets derived by the proposed approach is more accurate than other machine learning algorithm. (c) 2005 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2005.09.030
http://hdl.handle.net/11536/12106
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2005.09.030
期刊: EXPERT SYSTEMS WITH APPLICATIONS
Volume: 31
Issue: 1
起始頁: 174
結束頁: 183
顯示於類別:期刊論文


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