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- W2136332012 abstract "Two main research areas in statistical text categorization are similarity-based learning algorithms and associated thresholding strategies. The combination of these techniques significantly influences the overall performance of text categorization. After investigating two similarity-based classifiers (k-NN and Rocchio) and three common thresholding techniques (RCut, PCut, and SCut), we describe a new learning algorithm known as the keyword association network (KAN) and a new thresholding strategy (RinSCut) to improve performance over existing techniques. Extensive experiments have been conducted on the Reuters-21578 and 20-Newsgroups data sets. The experimental results show that our new approaches give better results for both micro-averaged F 1 and macro-averaged F 1 scores.KeywordsSimilarity ScoreText CategorizationMain Research AreaThresholding StrategyPositive DocumentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W2136332012 date "2002-01-01" @default.
- W2136332012 modified "2023-09-26" @default.
- W2136332012 title "A Comparative Study on Statistical Machine Learning Algorithms and Thresholding Strategies for Automatic Text Categorization" @default.
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- W2136332012 doi "https://doi.org/10.1007/3-540-45683-x_48" @default.
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