& In many data mining applications that address classification problems, feature and model selection are considered as key tasks. The appropriate input features of the classifier are selected from a given set of possible features, and the structure parameters of the classifier are adapted with respect to these features and a given dataset. This paper describes the particle swarm optimization algorithm (PSO) that performs feature and model selection simultaneously for the probabilistic neural network (PNN) classifier for power system disturbances. The probabilistic neural network is one of the successful classifiers used to solve many classification problems. However, the computational effort and storage requirement of the PNN method will prohibitively increase as the number of patterns used in the training set increases. An important issue that has not been given enough attention is the selection of a ''spread parameter,'' also called a ''smoothing parameter,'' in the PNN classifier. PSO is a powerful meta-heuristic technique in the artificial intelligence field; therefore, this study proposes a PSO-based approach, called PSO-PNN, to specify the beneficial features and the value of spread parameter to enhance the performance of PNN. The experimental results indicate that the proposed PSO-based approach significantly improves the classification accuracy with the discriminating input features for PNN.
& The significance of detection and classification of power quality (PQ) events that disturb the voltage and=or current waveforms in electrical power distribution networks is well known. Consequently, in spite of a large number of research reports in this area, research on the selection of useful features from the existing feature set and the parameter selection for specific classifiers has thus far not been explored. The choice of a smoothing parameter for a probabilistic neural network classifier (PNN) in the training process, together with feature selection, will significantly impact the classification accuracy. In this work, a thorough analysis is carried out, using two wrapper-based optimization techniques-the genetic algorithm and simulated annealing-for identifying the ensemble of celebrated features obtained using discrete wavelet transform together with the smoothing parameter selection of the PNN classifier. As a result of these analyses, the proper smoothing parameter together with a more useful feature set from among a wider set of features for the PNN classifier is obtained with improved classification accuracy. Furthermore, the results show that the performance of simulated annealing is better than the genetic algorithm for feature selection and parameter optimization in Power Quality Data Mining.
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