Gabrys, B. and Ruta, D., 2006. Genetic algorithms in classifier fusion. Applied Soft Computing, 6 (4), pp. 337-347.
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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.
|Additional Information:||In this paper we have focused on the challenge of many selection dimensions and decisions that need to be made when constructing well performing multiple classifier systems. Genetic algorithms have been identified as a very good technique for carrying out various types of parameter optimisation and multi-dimensional selection offering a good compromise between searching complexity and the quality of the found solutions. Among the discussed techniques, a new 3-dimensional fusion-classifier-feature selection model and a corresponding multidimensional GA based on the incidence cube representation have been proposed and discussed in detail. The new multidimensional representation of the solutions opens an opportunity for future development of a general framework with evolutionary mechanisms continuously applied to the working systems along many dimensions in order to accommodate the dynamically changing data sets and environments which are being pursued in a number of projects with Lufthansa Systems Berlin, Degussa and BT.|
|Uncontrolled Keywords:||Genetic algorithms, classification, classifier fusion, feature selection, classifier selection|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
Generalities > Computer Science and Informatics
|Group:||School of Design, Engineering & Computing > Smart Technology Research Centre|
|Deposited By:||INVALID USER|
|Deposited On:||17 Dec 2007|
|Last Modified:||07 Mar 2013 14:35|
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