Soft combination of neural classifiers: A comparative study

This paper presents four schemes for soft fusion of the outputs of multiple classi®ers. In the ®rst three approaches, the weights assigned to the classi®ers or groups of them are data dependent. The ®rst approach involves the calculation of fuzzy integrals. The second scheme performs weighted averaging with data-dependent weights. The third approach performs linear combination of the outputs of classi®ers via the BADD defuzzi®cation strategy. In the last scheme, the outputs of multiple classi®ers are combined using Zimmermann's compensatory operator. An empirical evaluation using widely accessible data sets substantiates the validity of the approaches with data-dependent weights, compared to various existing combination schemes of multiple classi®ers. Ó 1999 Elsevier Science B.V. All rights reserved.