Keywords: fuzzy classification, diversity, classifiers ensemble, feature selection, multi-objective genetic algorithms, nuclear transients, fault diagnosis, classification performance, static voting, feedwater systems, boiling water reactors, simulation, nuclear power plants, nuclear energy, transient classification, pattern recognition
Diagnosing faults in nuclear components by an ensemble of feature-diverse fuzzy classifiers
Ensembles of classifiers offer higher classification accuracy than single classifiers. One method for constructing an ensemble is to have the base classifiers work on different feature sets. In this paper, we present a method for selecting the feature sets of the base classifiers by means of a multi-objective genetic algorithm, aimed at maximising the classification performance and the diversity among the classifiers and at minimising the number of features in the subsets. A static voting technique is used to effectively combine the outputs of the base classifiers to construct the ensemble output. The proposed approach is applied to the classification of (simulated) transients in the feedwater system of a boiling water reactor, and the results are compared with those obtained using an optimal single classifier.