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Abstract
This paper discusses the possibility of designing a two stage classifier for large-scale hierarchical and multilabel text classification task, that will be a compromise between two common approaches to this task. First of it is called big-bang, where there is only one classifier that aims to do all the job at once. Top-down approach is the second popular option, in which at each node of categories’ hierarchy, there is a flat classifier that makes a local classification between categories that are immediate descendants of that node. The article focuses on evaluating the performance of a k-NN algorithm at different levels of categories’ hierarchy, aiming to test, whether it will be profitable to make a semi-big-bang step (restricted to a specified level of the hierarchy), followed by a middle-down more detailed classification. Presented empirical experiments are done on Simple English Wikipedia dataset.
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- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- Advances in Neural Networks, Fuzzy Systems and Artificial Intelligence strony 88 - 94
- Language:
- English
- Publication year:
- 2014
- Bibliographic description:
- Draszawka K., Szymański J.: How Specific Can We Be with k-NN Classifier?// Advances in Neural Networks, Fuzzy Systems and Artificial Intelligence/ ed. Balicki Jerzy : WSEAS Press, 2014, s.88-94
- Verified by:
- Gdańsk University of Technology
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