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A Novel Approach Based on Associative Rule Mining Technique for Multi-label Classification (ARM-MLC)

Publication Type : Conference Proceedings

Publisher : Springer

Source : Part of the Advances in Intelligent Systems and Computing book series

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Campus : Amritapuri

School : School of Computing

Center : Computational Bioscience

Department : Computer Science

Year : 2021

Abstract : In this paper, we have implemented an efficient and novel technique for multi-label class prediction using associative rule mining. Many of the research works for the classification have been carried out on single-label datasets, but it is not useful for all real-world application accounting to multi-label datasets like scene classification, text categorization, etc. Hence, we propose an algorithm for performing multi-label classification and solve the problems which come across in the domain pertaining to single-label classification. Our novel technique (ARM-MLC) will aim in enhancing the accuracy of any decision-making processes. Here, in multi-label classification, based on our work, we aim to predict the multiple characters of the instances.

Cite this Research Publication : Prathibhamol, C. P., K. Ananthakrishnan, Neeraj Nandan, Abhijith Venugopal, and Nandu Ravindran. "A novel approach based on associative rule mining technique for multi-label classification (ARM-MLC)." In Progress in Advanced Computing and Intelligent Engineering, pp. 195-203. Springer, Singapore, 2021.

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