基于改进三体训练法的半监督专利文本分类方法

针对信息增益算法只能考察特征对整个系统的贡献、忽略特征对单个类别的信息贡献的问题，提出改进信息增益算法，通过引入权重系数调整对分类有重要价值的特征的信息增益值，以更好地考虑一个词在类别间的分布不均匀性. 针对传统专利自动分类中训练集标注瓶颈问题，提出基于改进三体训练算法的半监督分类方法，通过追踪每次更新后的训练集样本类别分布来动态改变3个分类器对同一未标记样本类别的预测概率阈值，从而在降低噪音数据影响的同时实现对未标记训练样本的充分利用. 实验结果表明，本研究所提出的分类方法在有标记训练样本较少的情况下，可以取得较好的自动分类效果，并且适当增大未标记样本数据可以增强分类器的泛化能力.

Semi-supervised patent text classification method based on improved Tri-training algorithm

An improved information gain (IG) algorithm was proposed, in order to solve the problem that the IG algorithm can only be used to investigate the contribution of features to the whole system, but not for a single category. The weight coefficient is introduced to adjust the information gain values of features important for classification, so the inhomogeneity of distribution of a word among categories can be better considered. A semi-supervised classification method based on the improved Tri-training algorithm was proposed, aiming at the bottleneck problem of training set labeling in traditional patent automatic classification. The prediction probability thresholds of the same unlabeled sample's category of three classifiers are dynamically changed by tracking the distribution of sample categories of training sets after each iteration. As a result, the influence of noise data is reduced and the full advantage of the unmarked training samples is achieved. Results indicate that the proposed classification method has positive automatic classification effect in the case of fewer labeled training samples, and the generalization ability of the classifier can be improved through appropriately increasing unlabeled sample data.