基于粒度条件熵的部分标注数据属性约简
Granular conditional entropy-based attribute reduction for partially labeled data with proxy labels
摘要：
属性约简是粗糙集理论中的一个重要研究课题，基于粗糙集理论的属性约简方法已经被提出。然而，它们中的大多数是专门为处理标记数据或未标记数据而设计的，而许多现实世界的应用程序是以部分监督的形式出现的。本文提出了一种基于粗糙集的部分标记数据半监督属性约简方法。特别地，借助于数据的先验类分布信息，我们首先开发了一种简单而有效的策略来产生未标记数据的代理标签。然后将信息粒度的概念引入到信息论测度中，在此基础上提出了一种新的粒度条件熵测度，并从理论上证明了其单调性。此外，提出了一种快速启发式算法来生成部分标记数据的最优约简，该算法通过去除不相关的样本和排除冗余属性来加速属性约简过程.在UCI数据集上进行的大量实验表明，所提出的半监督属性约简方法是有前途的，甚至在分类性能方面与标记数据和具有真实标签的未标记数据的监督方法相媲美。
英文摘要：
Attribute reduction is one of the most important research topics in the theory of rough sets, and many rough sets-based attribute reduction methods have thus been presented. However, most of them are specifically designed for dealing with either labeled data or unlabeled data, while many real-world applications come in the form of partial supervision. In this paper, we propose a rough sets-based semi-supervised attribute reduction method for partially labeled data. Particularly, with the aid of prior class distribution information about data, we first develop a simple yet effective strategy to produce the proxy labels for unlabeled data. Then the concept of information granularity is integrated into the information-theoretic measure, based on which, a novel granular conditional entropy measure is proposed, and its monotonicity is proved in theory. Furthermore, a fast heuristic algorithm is provided to generate the optimal reduct of partially labeled data, which could accelerate the process of attribute reduction by removing irrelevant examples and excluding redundant attributes simultaneously. Extensive experiments conducted on UCI data sets demonstrate that the proposed semi-supervised attribute reduction method is promising and even compares favourably with the supervised methods on labeled data and unlabeled data with true labels in terms of classification performance.
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