Abstract
A genetic semi-supervised fuzzy clustering algorithm is proposed, which can learn text classifier from labeled and unlabeled documents. Labeled documents are used to guide the evolution process of each chromosome, which is fuzzy partition on unlabeled documents. The fitness of each chromosome is evaluated with a combination of fuzzy within cluster variance of unlabeled documents and misclassification error of labeled documents. The structure of the clusters obtained can be used to classify future new documents. Experimental results show that the proposed approach can improve text classi-fication accuracy significantly, compared to text classifiers trained with a small number of labeled documents only. Also, this approach performs at least as well as the similar approach – EM with Naïve Bayes
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Liu, H., Huang, St. (2003). A Genetic Semi-supervised Fuzzy Clustering Approach to Text Classification. In: Dong, G., Tang, C., Wang, W. (eds) Advances in Web-Age Information Management. WAIM 2003. Lecture Notes in Computer Science, vol 2762. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45160-0_17
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DOI: https://doi.org/10.1007/978-3-540-45160-0_17
Publisher Name: Springer, Berlin, Heidelberg
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