Subspace based feature selection for pattern recognition
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Design of input vector for day-ahead price forecasting of electricity markets
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems with Applications: An International Journal
Feature subset selection wrapper based on mutual information and rough sets
Expert Systems with Applications: An International Journal
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Input feature selection for classification problems
IEEE Transactions on Neural Networks
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One of the most important issues in the PQ assessment is diagnosis of Power Quality Disturbances (PQDs) using an effectual low computational burden strategy. We recommend a new approach for PQ analysis that addresses several major problems of prior works, including algorithm execution time, computational complexity, and accuracy. This paper suggests an effective and comprehensive method, so-called ''integrated approach'', for extracting features using integration of discrete wavelet transform and hyperbolic S transform. Moreover, a comparative assessment of PQDs recognition using various combinations of different Feature Selection (FS) and classification methods is presented. FS can reduce the dimension of feature space which leads to better performance of detection system. Four well-known FS techniques namely modified relief, mutual information, sequential forward selection, sequential backward selection and three benchmark classifiers, are considered. The particle swarm optimization is used to obtain optimal parameters of these classifiers. The key attribute of this paper is that it yields good time-frequency resolution with low computation burden for optimal PQ monitoring structure. Empirical results show that the proposed structures can yield an automatic online/offline monitoring of PQ with sparser structures and less computational execution time, both in the training and recognition phases, without sacrificing generality of performance.