Supervised Classification Using Balanced Training

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http://hdl.handle.net/10138/153192

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Du , M , Pierce , M , Pivovarova , L & Yangarber , R 2014 , Supervised Classification Using Balanced Training . in Unknown host publication . Lecture notes in artificial intelligence , no. 8791 , Springer-Verlag , International Conference on Statistical Language and Speech Processing (SLSP 2014) , Grenoble , France , 14/10/2014 . < http://grammars.grlmc.com/slsp2014/ >

Titel: Supervised Classification Using Balanced Training
Författare: Du, Mian; Pierce, Matthew; Pivovarova, Lidia; Yangarber, Roman
Medarbetare: University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
Utgivare: Springer-Verlag
Datum: 2014-10
Språk: eng
Sidantal: 12
Tillhör serie: Unknown host publication
Tillhör serie: Lecture notes in artificial intelligence
Permanenta länken (URI): http://hdl.handle.net/10138/153192
Abstrakt: We examine supervised learning for multi-class, multi-label text classification. We are interested in exploring classification in a real-world setting, where the distribution of labels may change dynamically over time. First, we compare the performance of an array of binary classifiers trained on the label distribution found in the original corpus against classifiers trained on balanced data, where we try to make the label distribution as nearly uniform as possible. We discuss the performance trade-offs between balanced vs. unbalanced training, and highlight the advantages of balancing the training set. Second, we compare the performance of two classifiers, Naive Bayes and SVM, with several feature-selection methods, using balanced training. We combine a Named-Entity-based rote classifier with the statistical classifiers to obtain better performance than either method alone.
Subject: 113 Computer and information sciences
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