Title: Selection of statistical wavelet features using a wrapper approach for electrical appliances identification based on a KNN classifier combined with voting rules method

Authors: Fateh Ghazali; Abdenour Hacine-Gharbi; Philippe Ravier

Addresses: LMSE Laboratory, University of Bordj Bou Arreridj, Elanasser, 34030, Algeria ' LMSE Laboratory, University of Bordj Bou Arreridj, Elanasser, 34030, Algeria ' PRISME Laboratory, University of Orleans, 12 rue de Blois, 45067 Orléans, France

Abstract: This work is an extended version of a paper presented in the International Conference on Intelligent Systems and Patterns Recognition where the authors have proposed a compact features representation based on the estimation of statistical features using discrete wavelet transform for electrical appliances identification based on a K nearest neighbour classifier combined with voting rule strategy. The results have shown that the wavelet cepstral coefficients (WCC) descriptor presents highest performance with 98.13% classification rate (CR). In this work, we propose many extensions: 1) The logarithm energy (LOG_E) is used as additional descriptor; 2) The relevance of the wavelet-based features combined with LOG_E descriptor is investigated using feature selection based on wrappers approach; 3) Deep performances evaluation is carried out using five additional metrics. The results show that the selection of four features of WCC combined with LOG_E improves the CR at 98.51%.

Keywords: electrical appliances identification; statistical feature extraction; discrete wavelet analysis; K nearest neighbour classifier; voting rule method; wrapper feature selection approach.

DOI: 10.1504/IJCSYSE.2021.121354

International Journal of Computational Systems Engineering, 2021 Vol.6 No.5, pp.220 - 230

Received: 01 Nov 2020
Accepted: 19 Apr 2021
Published online: 07 Mar 2022 *

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