Abstract
Symbolic classifiers from Artificial Intelligence compete with those from the established and emerging fields of statistics and neural networks. Traditional view is that symbolic classifiers are good in that they are easier to use, are faster, and produce human understandable rules. However, as this paper shows, through a comparison of fourteen established state-of-the-art symbolic, statistical, and neural classifiers on eight large real-world problems, symbolic classifiers also have superior, or at least comparable, accuracy performance when the characteristics of the data suit them. These data characteristics are measured using a set of statistical and qualitative descriptors, first proposed in the present (and the related) work. This has implications for algorithm users and method designers, in that the strength of various algorithms can be exploited in application, and in that superior features of other algorithms can be incorporated into existing algorithms.
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© 1994 Springer-Verlag New York, Inc.
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Feng, C., King, R., Sutherland, A., Muggleton, S., Henery, R. (1994). Symbolic Classifiers: Conditions to Have Good Accuracy Performance. 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_38
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DOI: https://doi.org/10.1007/978-1-4612-2660-4_38
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