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Information Journal Paper

Title

ML-CK-ELM: An efficient multi-layer extreme learning machine using combined kernels for multi-label classification

Pages

  3005-3018

Abstract

 Recently, many Neural network methods have been proposed for multilabel classification in the literature. One of these recent methods is the Multi-Layer Extreme learning machines (ML-ELMs) in which stack auto encoders are used for tuning their weights. However, ML-ELMs suffer from three primary drawbacks: First, input weights and biases are chosen randomly; second, the pseudoinverse solution for calculating output weights will increase the reconstruction error; third, memory and execution time of transformation matrices are proportional to the number of hidden layers. In this paper, Multi-Layer Kernel Extreme learning machine (ML-CK-ELM) that uses a Linear combination of base kernels in each layer is proposed for multi-label classification. The proposed approach effectively addresses the above-mentioned drawbacks. Furthermore, multi-label classification data are inherently characterized by multi-modal aspects due to a variety of labels assigned to each instance. Applying a combination of different kernels is the added advantage of ML-CK-ELM that implicitly assesses the inherent multi-modal aspects of multi-label data; each kernel can be effectively used to cover one of the modals better than other kernels. The empirical study indicates that ML-CK-ELM shows competitively better performance than other state-of-the-art methods, and experimental results of multilabel datasets verify the feasibility of ML-CK-ELM.

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    APA: Copy

    Rezaei Ravari, m., EFTEKHARI, M., & Saberi Movahed, f.. (2020). ML-CK-ELM: An efficient multi-layer extreme learning machine using combined kernels for multi-label classification. SCIENTIA IRANICA, 27(6 (Transactions D: Computer Science and Engineering and Electrical Engineering)), 3005-3018. SID. https://sid.ir/paper/973900/en

    Vancouver: Copy

    Rezaei Ravari m., EFTEKHARI M., Saberi Movahed f.. ML-CK-ELM: An efficient multi-layer extreme learning machine using combined kernels for multi-label classification. SCIENTIA IRANICA[Internet]. 2020;27(6 (Transactions D: Computer Science and Engineering and Electrical Engineering)):3005-3018. Available from: https://sid.ir/paper/973900/en

    IEEE: Copy

    m. Rezaei Ravari, M. EFTEKHARI, and f. Saberi Movahed, “ML-CK-ELM: An efficient multi-layer extreme learning machine using combined kernels for multi-label classification,” SCIENTIA IRANICA, vol. 27, no. 6 (Transactions D: Computer Science and Engineering and Electrical Engineering), pp. 3005–3018, 2020, [Online]. Available: https://sid.ir/paper/973900/en

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