Despite the sizable achievements obtained, the use of soft classi®ers is still limited by the lack of well-assessed and
adequate methods for evaluating the accuracy of their outputs. This paper proposes a new method that uses the fuzzy set theory to extend the applicability of the traditional error matrix method to the evaluation of soft classi®ers. It is designed to cope with those situations in which classi®cation and/or reference data are expressed in multimembership form and the grades of membership represent dierent levels of approximation to intrinsically vague classes.
In many applications, it is desirable to have a“soft” classi®er that, for a given input pattern vector, computes the “likelihood” that the pattern lies in any of a set of possible classes. In general, soft models for classi®cation are rooted in speci®c representation frameworks within which the partial belongingness of a given pattern to several categories at the same time is explicitly modeled (Binaghi et al., 1996; Bouchon-Meunier et al., 1995). Statistical classi®cation models interpret a given pattern as fully contributing to a given class, and the computed probabilities are an expression of the frequency with which this full membership occurs.
Publisher:ITIM, C.N.R., IRRS, C.N.R.
By: Elisabetta Binaghi , Pietro . Brivio , Paolo Ghezzi , Anna Rampini
File information: English Language / 14 Page / Size : 662 Kb
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ناشر: ITIM, C.N.R., IRRS, C.N.R.
کاری از: Elisabetta Binaghi , Pietro . Brivio , Paolo Ghezzi , Anna Rampini
اطلاعات فایل: زبان انگلیسی/ 14 صفحه/ حجم 662 کیلوبایت
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