主动协同半监督粗糙集分类模型

粗糙集理论是一种有监督学习模型，一般需要适量有标记的数据来训练分类器。但现实一些问题往往存在大量无标记的数据，而有标记数据由于标记代价过大较为稀少。文中结合主动学习和协同训练理论，提出一种可有效利用无标记数据提升分类性能的半监督粗糙集模型。该模型利用半监督属性约简算法提取两个差异性较大的约简构造基分类器，然后基于主动学习思想在无标记数据中选择两分类器分歧较大的样本进行人工标注，并将更新后的分类器交互协同学习。UCI数据集实验对比分析表明，该模型能明显提高分类学习性能，甚至能达到数据集的最优值。

A Semi-Supervised Rough Set Model for Classification Based on Active Learning and Co-Training

Rough set theory, as an effective supervised learning model, usually relies on the availability of an amount of labeled data to train the classifier. Howerer, in many practical problems, large amount of unlabeled data are readily available, and labeled ones are fairly expensive to obtain because of high cost. In this paper, a semi-supervised rough set model is proposed to deal with the partially labeled data. The proposed model firstly employs two diverse semi-supervised reducts to train its base classifiers on labeled data. The unlabeled ramified samples for two base classifiers are selected to be labeled based on the principle of active learning, and then the updated classifiers learn from each other by labeling confident unlabeled samples to its concomitant. The experimental results on selected UCI datasets show that the proposed model greatly improves the classification performance of partially labeled data, and even the best performance of dataset is obtained.