Binary transformation method for multi-label stream classification

Date
2022-10-17
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Publisher
Association for Computing Machinery
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Pages
3968 - 3972
Language
English
Type
Conference Paper
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Abstract

Data streams produce extensive data with high throughput from various domains and require copious amounts of computational resources and energy. Many data streams are generated as multi-labeled and classifying this data is computationally demanding. Some of the most well-known methods for Multi-Label Stream Classification are Problem Transformation schemes; however, previous work on this area does not satisfy the efficiency demands of multi-label data streams. In this study, we propose a novel Problem Transformation method for Multi-Label Stream Classification called Binary Transformation, which utilizes regression algorithms by transforming the labels into a continuous value. We compare our method against three of the leading problem transformation methods using eight datasets. Our results show that Binary Transformation achieves statistically similar effectiveness and provides a much higher level of efficiency.

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Keywords
Data stream, Classification, Multi-label, Problem transformation
Citation
Published Version (Please cite this version)