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A novel classification approach based on Extreme Learning Machine and Wavelet Neural Networks

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Abstract

In this paper, we present a novel classification approach based on Extreme Learning Machine (ELM) and Wavelet Neural Networks. We introduce two novel contributions. The first is Extreme Learning Machine based on Fast Wavelet Transform (ELM-FWT) algorithm aiming to solve the problem of matrix inversion which represents several limitations to ELM method. The second contribution is dedicated to solving another problem of machine learning algorithms, i.e. the number of neurons in the hidden nodes. It consists in allocating automatically and efficiently the number of neurons in the hidden layer. To demonstrate the effectiveness of our proposal, we realized numerous experiments and comparisons using 19 algorithms from Breast Cancer Wisconsin database which are considered as references in machine learning. Experimental results clearly demonstrate the stability and robustness of the proposed approach.

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Authors and Affiliations

  1. RTIM:Research Team in Intelligent Machines, University of Gabes, National Engineering School of Gabes, Gabes, Tunisia

    Siwar Yahia, Salwa Said & Mourad Zaied

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  1. Siwar Yahia
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  2. Salwa Said
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  3. Mourad Zaied
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Correspondence to Siwar Yahia.

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Yahia, S., Said, S. & Zaied, M. A novel classification approach based on Extreme Learning Machine and Wavelet Neural Networks. Multimed Tools Appl 79, 13869–13890 (2020). https://doi.org/10.1007/s11042-019-08248-y

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  • DOI: https://doi.org/10.1007/s11042-019-08248-y

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