Benchmarking Multi-label Classification Algorithms

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Title: Benchmarking Multi-label Classification Algorithms
Authors: Pakrashi, Arjun
Greene, Derek
MacNamee, Brian
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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 learningStatistics
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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
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