CAJ | 학술논문

在实际应用中,由于各种原因时常无法直接获得已标识反例,导致传统分类方法暂时失灵,因此,基于正例和未标识集的半监督学习顿时成了理论界研究的热点.研究者们提出了不同的解决方法,然而,这些方法都不能有效处理非平衡的分类问题,尤其当隐匿反例非常少或训练集中的实例分布不均匀时.因此,提出了一种基于KL距离的半监督分类算法--LiKL:依次挖掘出未标识集中的最可靠正例和反例,接着使用训练好的增强型分类器来分类.与其他方法相比,不仅提高了分类的查准率和查全率,而且具有鲁棒性.
재실제응용중,유우각충원인시상무법직접획득이표식반례,도치전통분류방법잠시실령,인차,기우정례화미표식집적반감독학습돈시성료이론계연구적열점.연구자문제출료불동적해결방법,연이,저사방법도불능유효처리비평형적분류문제,우기당은닉반례비상소혹훈련집중적실례분포불균균시.인차,제출료일충기우KL거리적반감독분류산법--LiKL:의차알굴출미표식집중적최가고정례화반례,접착사용훈련호적증강형분류기래분류.여기타방법상비,불부제고료분류적사준솔화사전솔,이차구유로봉성.
In many real applications, it's often difficult or quite expensive to get labeled negative examples for learning, such as Web search, medical diagnosis, earthquake identification and so on. This situation makes the traditional classification techniques work ineffectively, because the precondition that every class has to own its labeled instances is not met. Therefore, the semi-supervised learning method from positive and unlabeled data becomes a hot topic in the literature. In the past years, researchers have proposed many methods, but they can't cope well with the imbalanced classification problem, especially when the number of hidden negative examples in the unlabeled set is relatively small or the distribution of training examples in the training set becomes quite different. In this paper, a novel KL divergence-based semi-supervised classification algorithm, named LiKL (i.e. semi-supervised learning algorithm from imbalanced data based on KL divergence), is proposed to tackle this special problem. The proposed approach firstly finds likely positive examples existing in the unlabeled set, and successively finds likely negative ones, followed by an enhanced logistic regression classifier to classify the unlabeled set. The experiments show that the proposed approach not only improves precision and recall, but also is very robust, compared with former work in the literature.