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Advisor(s)
Abstract(s)
Text classification is generally the process of extracting interesting and non-trivial information and knowledge from text. One of the main problems with text classification systems is the lack of labeled data, as well as the cost of labeling unlabeled data. Thus, there is a growing interest in exploring the use of unlabeled data as a way to improve classification performance in text classification. The ready availability of this kind of data in most applications makes it an appealing source of information. In this work we propose an Incremental Background Knowledge (IBK) technique to introduce unlabeled data into the training set by expanding it using initial classifiers to deliver oracle decisions. The defined incremental SVM margin-based method was tested in the Reuters-21578 benchmark showing promising results.
Description
19th International Conference on Artificial Neural Networks, ICANN 2009, 14 September 2009 through 17 September 2009 - Code 77563
Keywords
Support Vector Machine Text Categorization Unlabeled Data Basic Background Knowledge Binary Class Problem
Citation
Silva, C., Ribeiro, B. (2009). Improving Text Classification Performance with Incremental Background Knowledge. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_95.
Publisher
Springer
Collections
CC License
Without CC licence