A fuzzy set-based accuracy assessment of soft classi®cation

توضیحات

ABSTRACT

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 di€erent levels of approximation to intrinsically vague classes.

INTRODUCTION

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.

Year: 1999

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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سال: 1999

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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