An Anti-noise Text Categorization Method Based on Support Vector Machines

With the rapid growth of online information, text categorization has become one of the key techniques for handling and organizing text data. Though the native features of SVM (Support Vector Machines) are better than Naïve Bayes’ for text categorization in theory, the classification precision of SVM is lower than Bayesian method in real world. This paper tries to find out the mysteries by analyzing the shortages of SVM, and presents an anti-noise SVM method. The improved method has two characteristics: 1) It chooses the classification space by defining the optimal n-dimension classifying hyperspace. 2) It separates noise samples by preprocessing, and trains the classifier using noise free samples. Compared with naïve Bayes method, the classification precision of anti-noise SVM is increased about 3 to 9 percent.