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%.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Computational Systems Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
