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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/37390

Title: Least squares support vector machine with self-organizing multiple kernel learning and sparsity
Authors: Liu, Chang
Tang, Lixin
Liu, Jiyin
Keywords: Least squares support vector machines
Self-organizing multiple kernel learning
Sparse selection
Differential evolution
Issue Date: 2019
Publisher: © Elsevier
Citation: LIU, C., TANG, L. and LIU, J., 2019. Least squares support vector machine with self-organizing multiple kernel learning and sparsity. Neurocomputing, 331, pp. 493 - 504.
Abstract: © 2018 In recent years, least squares support vector machines (LSSVMs) with various kernel functions have been widely used in the field of machine learning. However, the selection of kernel functions is often ignored in practice. In this paper, an improved LSSVM method based on self-organizing multiple kernel learning is proposed for black-box problems. To strengthen the generalization ability of the LSSVM, some appropriate kernel functions are selected and the corresponding model parameters are optimized using a differential evolution algorithm based on an improved mutation strategy. Due to the large computation cost, a sparse selection strategy is developed to extract useful data and remove redundant data without loss of accuracy. To demonstrate the effectiveness of the proposed method, some benchmark problems from the UCI machine learning repository are tested. The results show that the proposed method performs better than other state-of-the-art methods. In addition, to verify the practicability of the proposed method, it is applied to a real-world converter steelmaking process. The results illustrate that the proposed model can precisely predict the molten steel quality and satisfy the actual production demand.
Description: This paper is in closed access until 25th Nov 2019.
Sponsor: This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0901900, in part by the Fund for Innovative Research Groups of the National Natural Science Foundation of China under Grant 71621061, in part by the National Natural Science Foundation of China through the Major International Joint Research Project under Grant 71520107004, in part by the Major Program of National Natural Science Foundation of China under Grant 71790614, in part by the 111 Project under Grant B16009, and in part by the National Natural Science Foundation of China under Grants 61702077.
Version: Accepted for publication
DOI: 10.1016/j.neucom.2018.11.067
URI: https://dspace.lboro.ac.uk/2134/37390
Publisher Link: https://doi.org/10.1016/j.neucom.2018.11.067
ISSN: 0925-2312
Appears in Collections:Closed Access (Business)

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