Fuzzy-Rough Set based Semi-Supervised Learning

Abstract—Much work has been carried out in the area of fuzzy-rough sets for supervised learning. However, very little has been accomplished for the unsupervised or semi-supervised tasks. For many real-word applications, it is often expensive, time-consuming and difficult to obtain labels for all data objects. This often results in large quantities of data which may only have very few labelled data objects. This paper proposes a novel fuzzy-rough based semi-supervised self-learning or self-training approach for the assignment of labels to unlabelled data. Unlike other semi-supervised approaches, the proposed technique requires no subjective thresholding or domain information. An experimental evaluation is performed on artificial data and also applied to a real-world mammographic risk assessment problem with encouraging results. Index Terms—Rough sets, fuzzy sets, mammographic analysis, semi-supervised learning I

Fuzzy Rough Sets for Self-Labelling: an Exploratory Analysis

Semi-supervised learning incorporates aspects of both supervised and unsupervised learning. In semi-supervised classification, only some data instances have associated class labels, while others are unlabelled. One particular group of semi-supervised classification approaches are those known as self-labelling techniques, which attempt to assign class labels to the unlabelled data instances. This is achieved by using the class predictions based upon the information of the labelled part of the data. In this paper, the applicability and suitability of fuzzy rough set theory for the task of self-labelling is investigated. An important preparatory experimental study is presented that evaluates how accurately different fuzzy rough set models can predict the classes of unlabelled data instances for semi-supervised classification. The predictions are made either by considering only the labelled data instances or by involving the unlabelled data instances as well. A stability analysis of the predictions also helps to provide further insight into the characteristics of the different fuzzy rough models. Our study shows that the ordered weighted average based fuzzy rough model performs best in terms of both accuracy and stability. Our conclusions offer a solid foundation and rationale that will allow the construction of a fuzzy rough self-labelling technique. They also provide an understanding of the applicability of fuzzy rough sets for the task of semi-supervised classification in general