Large-Scale Data Classification Method Based on Machine Learning Model

Abstract

Classification is to map the data item in the database into a given class. It is an important research direction in data mining. In allusion to the shortcomings of traditional classification methods, such as the decision tree, K nearest neighbor, Bayes , fuzzy logic, genetic algorithms and neural networks and so on, the support vector machine with perfect theory, strong adaptability, global optimization, short training time, good generalization performance is introduced into the classification, a machine learning model based on the SMO algorithm and RBF kernel function of the SVM is proposed to realize a classification method in this paper. This method transforms the nonlinear classification problem into linear classification problem by improving the data dimension. It can better solve the problems of the minimum error in the training set and the larger error in the test set in the traditional algorithm. Application of UCI classification experiment shows that the proposed method takes on the better convergence, faster training speed and higher classification accuracy.