Benchmarking Multi-label Classification Algorithms
|Title:||Benchmarking Multi-label Classification Algorithms||Authors:||Pakrashi, Arjun
|Permanent link:||http://hdl.handle.net/10197/8311||Date:||21-Sep-2016||Abstract:||Multi-label classification is an approach to classification prob- lems that allows each data point to be assigned to more than one class at the same time. Real life machine learning problems are often multi-label in nature—for example image labelling, topic identification in texts, and gene expression prediction. Many multi-label classification algorithms have been proposed in the literature and, although there have been some benchmarking experiments, many questions still remain about which ap- proaches perform best for certain kinds of multi-label datasets. This pa- per presents a comprehensive benchmark experiment of eleven multi- label classification algorithms on eleven different datasets. Unlike many existing studies, we perform detailed parameter tuning for each algorithm- dataset pair so as to allow a fair comparative analysis of the algorithms. Also, we report on a preliminary experiment which seeks to understand how the performance of different multi-label classification algorithms changes as the characteristics of multi-label datasets are adjusted.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||CEUR Workshop Proceedings||Copyright (published version):||2016 the Authors||Keywords:||Machine learning; Statistics||Other versions:||http://ceur-ws.org/Vol-1751/||Language:||en||Status of Item:||Peer reviewed||Is part of:||Greene, D., Mac Namee, B. and Ross, R. (eds). Proceedings of the 24th Irish Conference on Artificial Intelligence and Cognitive Science||Conference Details:||24th Irish Conference on Artificial Intelligence and Cognitive Science (AICS'16), Dublin, Ireland, 20-21 September 2016|
|Appears in Collections:||Insight Research Collection|
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