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KNOWLEDGE‐BASED FEATURE DISCOVERY FOR EVALUATION FUNCTIONS
Author(s) -
Fawcett Tom E.
Publication year - 1996
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.1996.tb00252.x
Subject(s) - computer science , feature (linguistics) , artificial intelligence , set (abstract data type) , domain (mathematical analysis) , machine learning , evaluation function , function (biology) , domain knowledge , data mining , mathematics , mathematical analysis , philosophy , linguistics , evolutionary biology , biology , programming language
Since Samuel's work on checkers over thirty years ago, much effort has been devoted to learning evaluation functions. However, all such methods are sensitive to the feature set chosen to represent the examples. If the features do not capture aspects of the examples significant for problem solving, the learned evaluation function may be inaccurate or inconsistent. Typically, good feature sets are carefully handcrafted and a great deal of time and effort goes into refining and tuning them. This paper presents an automatic knowledge‐based method for generating features for evaluation functions. The feature set is developed iteratively: features are generated, then evaluated, and this information is used to develop new features in turn. Both the contribution of a feature and its computational expense are considered in determining whether and how to develop it further. This method has been applied to two problem‐solving domains: the Othello board game and the domain of telecommunications network management. Empirical results show that the method is able to generate many known features and several novel features and to improve concept accuracy in both domains.