Construction of classifier ensembles by means of artificial immune systems

to the design of classifier ensembles. Ensembles of classifiers are a very interesting alter-native to single classifiers when facing difficult problems. In general, ensembles are able to achieve better performance in terms of learning and generalisation errors. Several papers have shown that the processes of classifier design and combination must be related in order to obtain better ensembles. Artificial Immune Systems are a recent paradigm based on the immune systems of animals. The features of this new paradigm make it very appropriate for the design of systems where many compo-nents must cooperate to solve a given task. The design of classifier ensembles can be considered within such a group of systems, as the cooperation of the individual classifiers is able to improve the performance of the overall system. This paper studies the viability of Artificial Immune Systems when dealing with ensemble design. We construct a population of classifiers that is evolved using an Artificial Immune algorithm. From this population of classifiers several different en-sembles can be extracted. These ensembles are favourably compared with ensembles obtained using standard methods in 35 real-world classification problems from th