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Text Classification based on a Feature Projection Technique with Robustness from Noisy Data  

고영중 (서강대학교 산업기술연구소)
서정연 (서강대학교)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.4, 2004 , pp. 498-504 More about this Journal
Abstract
This paper presents a new text classifier based on a feature projection technique. In feature projections, training documents are represented as the projections on each feature. A classification process is based on individual feature projections. The final classification is determined by the sum from the individual classification of each feature. In our experiments, the proposed classifier showed high performance. Especially, it have fast execution speed and robustness with noisy data in comparison with k-NN and SVM, which are among the state-of-art text classifiers. Since the algorithm of the proposed classifier is very simple, its implementation and training process can be done very simply. Therefore, it can be a useful classifier in text classification tasks which need fast execution speed, robustness, and high performance.
Keywords
Text Classification; Feature Projections; Text Classifier;
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