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Integration of human knowledge and machine knowledge by using fuzzy post adjustment: its performance in stock market timing prediction
Author(s) -
Lee Kun Chang,
Kim Won Chul
Publication year - 1995
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.1995.tb00270.x
Subject(s) - computer science , expert system , machine learning , artificial intelligence , legal expert system , domain knowledge , knowledge integration , fuzzy logic , inference , subject matter expert , knowledge based systems , stock market , data mining , knowledge management , paleontology , horse , biology
This paper proposes a fuzzy post adjustment (FPA) mechanism so that human knowledge and machine knowledge can be integrated more synergistically to improve the performance of expert systems. Machine knowledge means knowledge algorithmically derived from past instances. Human knowledge implies (1) expert knowledge judging the trends of external factors and (2) user knowledge representing users’personal views about information given by both expert knowledge and machine knowledge. We consider an expert system that uses the FPA mechanism to incorporate the effect of external factors effectively into its inference process. The goal of this expert system is stock market timing prediction, which is divided into four kinds: bull, edged‐up, edged‐down and bear. Empirical tests showed that the proposed FPA mechanism can improve the performance of an expert system significantly, even in a turbulent decision‐making environment.