CAJ | 학술논문

针对Web中文文本分类中现有权重计算方法的不足和支持向量机算法对大数据量模式分类的低效性，提出了基于粗糙集约简并且加权的支持向量机分类方法；粗糙集作为支持向量机分类的前期预处理器，应用粗糙集的约简理论和基于Web中文文本的可变精度粗糙集加权方法对分类前的数据分别进行简化并计算权重，从而提高支持向量机后期分类的效率和精度；实验结果表明，支持向量机对约简并加权后的数据进行分类，分类性能得到了进一步保证。
침대Web중문문본분류중현유권중계산방법적불족화지지향량궤산법대대수거량모식분류적저효성，제출료기우조조집약간병차가권적지지향량궤분류방법；조조집작위지지향량궤분류적전기예처리기，응용조조집적약간이론화기우Web중문문본적가변정도조조집가권방법대분류전적수거분별진행간화병계산권중，종이제고지지향량궤후기분류적효솔화정도；실험결과표명，지지향량궤대약간병가권후적수거진행분류，분류성능득도료진일보보증。
The existing weight computation methods and support vector machine (SVM) algorithm are low-efficiency in large amount of data pattern classification of Chinese web text, so that a classification algorithm of SVM based on rough set reduction and weighting is proposed in the paper. In the algorithm, rough set is used as preprocessor of SVM classification, and then the data is simplified with weight calculated using rough set reduction theory and variable precision rough set weighting method, in order to enhance the efficiency and precision of classification. The experimental data shows that the classification performance is further improved.