A Novel Multiclass Text Classification Algorithm Based on Multiconlitron

A novel multiclass text classification algorithm based on multiconlitron is proposed. The multiconlitron is constructed for each possible pair of classes in sample space, each of which is used to separate two classes. For the sample to be classified, every multiconlitron is used to judge its classman vote for the corresponding class. The final class of the sample is determined by the number of votes. The classification experiments are done on Reuters 21578, and the experimental results show that the proposed algorithm has better performance. Compared with multiclass support vector machines 1-a-1, 1-a-r and DAGSVM, while ensuring the classification accuracy, it improves the classification speed and training speed greatly. Introduction Multiclass text classification, which involves assigning one predefined categories to a free text according to its content, plays a key role in organizing the massive sources of unstructured text information into an organized state. With the rapid development of technology of computer and communication, there is a growing need for multiclass text categorization, because the sources of information to be managed, such as webpages, news sources, and database management, are increasing rapidly. Filtering of spam mail, quick search of interesting topics from large databases, and retrieving information based on user’s preferences from information sources, are some examples, where multiclass text categorization can play an important role. Therefore, automated text classification technology has attracted much attention and has become an important research area. Many multiclass text classification algorithms based on machine learning have been proposed and received better results [1-2]. Among supervised learning algorithms, SVM has been well explored, and has proven to be a promising technique for multiclass text categorization. Currently, main research results of multiclass SVM include one-against-rest [4], one-against-one [5], directed acyclic graph SVM (DAGSVM) [6] and so on. One-against-rest method uses less classifier with faster decision speed, while one-against-one method has higher classification accuracy, and DAGSVM performs more efficiency since it needn’t traverse all classifiers under the DAG combination strategy. Meanwhile, kernel function plays a key role in SVMs, and it can implicitly solve linear separable problem in high dimensional space. According to pattern recognition theory, linearly inseparable data in low dimensional space may achieve linearly separable in high dimensional feature space through the nonlinear mapping. But if the direct mapping is assigned, then there exists some difficulties such as determining the form and parameters of nonlinear mapping function, leading to serious "curse of dimensionality". The kernel function can solve the above problems very tactfully. It transforms the inner product in high dimensional feature space into function in low dimensional input space. However, selection of kernel function often lack specific guidance [7-11], and the demand for computing resources on large scale data is very big. In addition for kernel function, it is hard to interpret the change of metric space [12, 13]. 6th International Conference on Electronic, Mechanical, Information and Management (EMIM 2016) © 2016. The authors Published by Atlantis Press 843 Piecewise linear classifiers (PLCs) is a special non-linear classifiers. Its decision surface is composed of a number of hyperplanes. Therefore, compared with the general hypersurface, it is still simple. The parameters of PLCs is independent of the dataset, without assumptions statistical distribution of the sample. Meanwhile, it can approach hypersurfaces with various shapes, being a strong adaptive ability [14]. However, construction of the PLCs remains some problems. In general, determining the piecewise linear boundary is a complex global optimization problem [15]. In most cases, the search for such boundaries is transformed to minimize the error function about classification problem. Another goal for piecewise linear classifier is to select the appropriate number of hyperplanes. For the purpose, some training algorithms are designed and implemented. But they are usually very complicated and have lone running time, what limits the application of PLCs to a certain extent. To reduce training time and avoid solving the optimization problem, many heuristic methods are adopted, but they are often with more empirical operations, and not conducive to the promotion. Multiconlitron [16] absorbs the advantages of SVMs. It realizes classification with maximum margin but without kernel function and space mapping. Furthermore, it can minimize classification error on the training set, and dynamically obtain the number of hyperplanes. On the basis of multionlitron, a multiclass text classification algorithm(MCLP) is proposed. The algorithm will construct classifiers in input space without kernel function, and realize the approximation to true class boundary by piecewise linear functions .