关键词: 支持向量机, 特征选取, 学习策略, 优化方法, 阈值

Abstract: Support Vector Machine（SVM） is one of the important machine learning methods and applied successfully to solve many classifying problems in real life. Aiming to improve the classification accuracy and training efficient of SVM, this paper reviews different feature selection algorithms and learning strategies before training SVM according to classification procedure. At the same time, this paper compares the classification accuracy of different feature selection method such as SFS, IWSS, IWSSr and BARS, and analyzes two performance measures on classification accuracy and precision/recall breakeven point when active learning strategy and SVM are combined to obtain a classifier. Experimental results indicate that the accuracy could be significantly improved and the number of training sample could be dramatically reduced by integrating the filtering method into the wrapper method；and when labeled training sample size is too small, active learning obtains better accuracy, however, if passive learning wants to have the same accuracy as active learning, passive learning must have the six times training samples than active learning.

Key words: support vector machine, feature selection, learning strategy, optimization methodologies, threshold