Genetic algorithms in classifier fusion Bogdan Gabrys a, * , Dymitr Ruta b a Computational Intelligence Research Group, School of Design, Engineering and Computing, Bournemouth University, Talbot Campus, Fern Barrow, Poole BH12 5BB, UK b British Telecom, Research and Venturing, Adastral Park MLB1, pp12, Martlesham Heath, Ipswich IP5 3RE, UK Abstract An intense research around classifier fusion in recent years revealed that combining performance strongly depends on careful selection of classifiers to be combined. Classifier performance depends, in turn, on careful selection of features, which could be further restricted by the subspaces of the data domain. On the other hand, there is already a number of classifier fusion techniques available and the choice of the most suitable method depends back on the selections made within classifier, features and data spaces. In all these multidimensional selection tasks genetic algorithms (GA) appear to be one of the most suitable techniques providing reasonable balance between searching complexity and the performance of the solutions found. In this work, an attempt is made to revise the capability of genetic algorithms to be applied to selection across many dimensions of the classifier fusion process including data, features, classifiers and even classifier combiners. In the first of the discussed models the potential for combined classification improvement by GA-selected weights for the soft combining of classifier outputs has been investigated. The second of the proposed models describes a more general system where the specifically designed GA is applied to selection carried out simultaneously along many dimensions of the classifier fusion process. Both, the weighted soft combiners and the prototype of the three-dimensional fusion–classifier–feature selection model have been developed and tested using typical benchmark datasets and some comparative experimental results are also presented. # 2005 Elsevier B.V. All rights reserved. Keywords: Genetic algorithms; Classification; Classifier fusion; Feature selection; Classifier selection 1. Introduction Over a number of recent years an increasing scientific effort has been dedicated to the development and studies of multiple classifier systems (MCS) [17,18]. It has been frequently demonstrated that combing classifiers can offer significant classification performance improvement for a number of non-trivial pattern recognition problems [8,16,23]. There are many tools with implementations of many classifica- tion algorithms from statistics, machine learning, neural networks, fuzzy systems, and many other fields [6] which can be conveniently used and evaluated. As part of the experimentations using such tools offering many alternative classifiers one of the early approaches to building MCS was to ‘‘gather them all and combine’’ [17,24]. Various studies [23,17,11] have shown that just gathering as many classifiers as www.elsevier.com/locate/asoc Applied Soft Computing 6 (2006) 337–347 * Corresponding author. Fax: +44 1202 595314. E-mail addresses: bgabrys@bournemouth.ac.uk (B. Gabrys), dymitr.ruta@bt.com (D. Ruta). 1568-4946/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.asoc.2005.11.001