Fw_sfs: A Novel Feature Selection Method for Power Quality Data Mining

Ahila, R.; Sudhakaran, M.; Manimala, K.

doi:10.2316/journal.203.2010.2.203-4665

Cited by 2 publications

(1 citation statement)

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“…The filtering part, acting in the generic way similar to a filter method, ranks features without involving the classifier. The features with relatively high ranks are considered as informative feature candidates and then are restudied by the wrapper part that further investigates their contributions to a specific classifier (Ahila, Sudhakaran, and Manimala 2010). The third experiment had the objective to assess the capability of the proposed PSO-PNN classification system to boost the accuracy of the PNN classifier.…”

Section: Experimental Schemementioning

confidence: 99%

Particle Swarm Optimization-Based Feature Selection and Parameter Optimization for Power System Disturbances Classification

Ahila

Sadasivam

Manimala

2012

Applied Artificial Intelligence

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& 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.

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Section: Experimental Schemementioning

confidence: 99%