Soft combination of neural classifiers: A comparative study

Abstract

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.

DOI: 10.1016/S0167-8655(99)00012-4

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@article{Verikas1999SoftCO, title={Soft combination of neural classifiers: A comparative study}, author={Antanas Verikas and Arunas Lipnickas and Kerstin Malmqvist and Marija Bacauskiene and Adas Gelzinis}, journal={Pattern Recognition Letters}, year={1999}, volume={20}, pages={429-444} }