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A comparative study of fuzzy PSO and fuzzy SVD-based RBF neural network for multi-label classification

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Abstract

In multi-label classification problems, every instance is associated with multiple labels at the same time. Binary classification, multi-class classification and ordinal regression problems can be seen as unique cases of multi-label classification where each instance is assigned only one label. Text classification is the main application area of multi-label classification techniques. However, relevant works are found in areas like bioinformatics, medical diagnosis, scene classification and music categorization. There are two approaches to do multi-label classification: The first is an algorithm-independent approach or problem transformation in which multi-label problem is dealt by transforming the original problem into a set of single-label problems, and the second approach is algorithm adaptation, where specific algorithms have been proposed to solve multi-label classification problem. Through our work, we not only investigate various research works that have been conducted under algorithm adaptation for multi-label classification but also perform comparative study of two proposed algorithms. The first proposed algorithm is named as fuzzy PSO-based ML-RBF, which is the hybridization of fuzzy PSO and ML-RBF. The second proposed algorithm is named as FSVD-MLRBF that hybridizes fuzzy c-means clustering along with singular value decomposition. Both the proposed algorithms are applied to real-world datasets, i.e., yeast and scene dataset. The experimental results show that both the proposed algorithms meet or beat ML-RBF and ML-KNN when applied on the test datasets.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Rajiv Gandhi Proudyogiki Vishwavidhyalaya, Bhopal, M.P., India

    Shikha Agrawal, Jitendra Agrawal, Shilpy Kaur & Sanjeev Sharma

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  1. Shikha Agrawal
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  2. Jitendra Agrawal
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  3. Shilpy Kaur
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  4. Sanjeev Sharma
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Correspondence to Jitendra Agrawal.

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We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. We also confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed.

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Agrawal, S., Agrawal, J., Kaur, S. et al. A comparative study of fuzzy PSO and fuzzy SVD-based RBF neural network for multi-label classification. Neural Comput & Applic 29, 245–256 (2018). https://doi.org/10.1007/s00521-016-2446-x

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