Abstract
In this paper we present a method — knowledge-assisted discriminant analysis — to generate new comparative terms on symbolic and numeric attributes. The method has been implemented and tested on three real-world databases: mushroom classification, letter recognition, and liver disorder diagnosis. The experiment results show that combining AI and statistical techniques is an effective and efficient way to enhance a machine learning system’s concept description language in order to learn simple and comprehensible rules.
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Leng, B., Buchanan, B.G. (1994). Using knowledge-assisted discriminant analysis to generate new comparative terms. In: Cheeseman, P., Oldford, R.W. (eds) Selecting Models from Data. Lecture Notes in Statistics, vol 89. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2660-4_49
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DOI: https://doi.org/10.1007/978-1-4612-2660-4_49
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