Classification by means of fuzzy analogy-related proportions - A preliminary report
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings of the 2010 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2010, 2010, pp. 297 - 302
- Issue Date:
- 2010-12-01
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Boolean logic interpretations, as well as multiple-valued logic extensions, have been recently proposed for analogical proportions (i.e. statements of the form "a is to b as c is to d"), and for two other related formal proportions named reverse analogy ("what a is to b is the reverse of what c is to d"), and paralogy ("what a and b have in common c and d have it also"). These proportions relate items a, b, c, and d on the basis of their differences, or of their similarities. This may provide a basis for proposing a plausible classification for an object d described in terms of a set of features, on the basis of three other already classified objects described on the same features, considering that if some proportion holds for a sufficiently large number of features, it may hold on the allocation of the classes as well. This is the basis of a classification method which is tested on machine learning benchmarks for binary or multiple class problems with objects that have numerical features. © 2010 IEEE.
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