Research On Improved K Neighbor Support Vector Machine Algorithm Faced Text Classification

In the era of information explosion, facing the voluminous information, how to organize and manage these vast amounts of information, and obtain the necessary information quickly and accurately is still a serious problem. Text automatic classification is an effective solution, it can handle a large number of text messages and resolve the status of information disorder to a large extent address so that help users to easily and accurately grasp the information which they need.Support Vector Machine (SVM) is machine learning algorithm, which is built on the principle of structural risk minimization and the based theory of VC. For treatment of high-dimensional problem it has a large advantage because of its insensitive to the nature of feature relevance and sparse. Thus, the using of support vector machines in text categorization applications has great potential. However, using SVM often has the sample near the interface classification accuracy is not high.To address this shortcoming, it was proposed an improved KNN-SVM algorithm. By calculating the number of known categories of sample under different classification thresholds to automatically determine the optimal threshold, at the same time let the improved KNN algorithm merge into the support vector machines in an strive not to increase the time complexity of support vector machine basis, reduce the rate of wrong classification of the sample near the support vector hyperplanes. Finally, the improved KNN-SVM algorithm applied to the specific system of news classification, achieve text classification of a large number of news information and facilitate the user to read and browse news.