A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Floating search methods in feature selection
Pattern Recognition Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
A Direct Method of Nonparametric Measurement Selection
IEEE Transactions on Computers
Expert Systems with Applications: An International Journal
Subspace based feature selection for pattern recognition
Information Sciences: an International Journal
On the effectiveness of receptors in recognition systems
IEEE Transactions on Information Theory
An improved genetic algorithm for optimal feature subset selection from multi-character feature set
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A novel probabilistic feature selection method for text classification
Knowledge-Based Systems
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The significance of detection and classification of power quality (PQ) events that disturbs the voltage and/or current waveforms in the electrical power distribution networks is well known. Consequently, in spite of a large number of research reports in this area, the problem of PQ event classification remains to be an important engineering problem. Several feature construction, pattern recognition, analysis, and classification methods were proposed for this purpose. In spite of the extensive number of such alternatives, a research on the comparison of ''how useful these features with respect to each other using specific classifiers'' was omitted. In this work, a thorough analysis is carried out regarding the classification strengths of an ensemble of celebrated features. The feature items were selected from well-known tools such as spectral information, wavelet extrema across several decomposition levels, and local statistical variations of the waveform. The tests are repeated for classification of several types of real-life data acquired during line-to-ground arcing faults and voltage sags due to the induction motor starting under different load conditions. In order to avoid specificity in classifier strength determination, eight different approaches are applied, including the computationally costly ''exhaustive search'' together with the leave-one-out technique. To further avoid specificity of the feature for a given classifier, two classifiers (Bayes and SVM) are tested. As a result of these analyses, the more useful set among a wider set of features for each classifier is obtained. It is observed that classification accuracy improves by eliminating relatively useless feature items for both classifiers. Furthermore, the feature selection results somewhat change according to the classifier used. This observation shows that when a new analysis tool or a feature is developed and claimed to perform ''better'' than another, one should always indicate the matching classifier for the feature because that feature may prove comparably inefficient with other classifiers.