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.
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Acknowledgments
We, hereby, would like to express our gratitude towards the Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, Amritapuri Cam-pus, for furnishing their valuable time for the completion of our project.
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Prathibhamol, C.P., Ananthakrishnan, K., Nandan, N., Venugopal, A., Ravindran, N. (2021). A Novel Approach Based on Associative Rule Mining Technique for Multi-label Classification (ARM-MLC). In: Panigrahi, C.R., Pati, B., Mohapatra, P., Buyya, R., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1199. Springer, Singapore. https://doi.org/10.1007/978-981-15-6353-9_18
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DOI: https://doi.org/10.1007/978-981-15-6353-9_18
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