现有的Web主题分类算法一般基于单一模型构建或者仅仅把多个单一模型简单叠加进行决策。针对该问题，提出一种基于多分类器集成的改进AdaBoost算法的Web主题分类方法。算法先采用VIPS算法获取页面分块并获取其视觉特征和文本特征，根据每一类特征的维度分别训练弱分类器，然后计算其对应的错误率，修改错误判别的拒绝策略，从而针对不同特征产生相应的最优分类器，最后对两类最优分类器级联决策。实验结果表明，该方法能提高AdaBoost算法对复杂Web主题信息的分类准确率，同时也为Web主题分类领域的研究提供一种新的方案。%Current Web topic classification algorithms are generally constructed based on single model or merely superimpose the multiple single model for decision-making.In light of the problem, we propose a new Web topic classification method which is based on the modified multi-classifier integration AdaBoost algorithm .Firstly, the method uses VIPS algorithm to acquire page blocks as well as their visual and text features, and trains weak classifier on the basis of the dimension of each feature; then, the algorithm calculates its corresponding error rate and modifies the refusal strategies of error discrimination , so that generates the corresponding optimal classifier for different features ;finally it performs cascading decision-making on two kind of optimal classifiers .Experimental results demonstrate that the method can improve the classification precision of AdaBoost on complex Web topic information , and at the same time it also provides a kind of new scheme for research on Web topic classification field .
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