Integrating extended classifier system and knowledge extraction model for financial investment prediction: An empirical study

An-Pin Chen, Mu Yen Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

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 multiple 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.

Original languageEnglish
Pages (from-to)174-183
Number of pages10
JournalExpert Systems with Applications
Volume31
Issue number1
DOIs
StatePublished - 1 Jul 2006

Keywords

  • Extended classifier system
  • Knowledge extraction
  • Learning classifier system
  • Machine learning

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