Rough Sets

Ghali, N. I., W. G. Abd-Elmonim, and A. E. Hassanien, Object-Based Image Retrieval System Using Rough Set Approach, , London, Advances in Reasoning-Based Image Processing Intelligent Systems Intelligent Systems Reference Library, 2012, Volume 29, Part 2, 315-329, 2012. Abstract

In this chapter, we present an object-based image retrieval system using the rough set theory. The system incorporates two major modules: Pre-processing and Object-based image retrieval. In pre processing, an image based object segmentation algorithm in the context of the rough set theory is used to segment the images into meaningful semantic regions. A new object similarity measure is proposed for the image retrieval. Performance is evaluated on an image database and the effectiveness of proposed image retrieval system is demonstrated. Experimental results show that the proposed system performs well in terms of speed and accuracy.

Hassanien, A. E., "Rough set approach for attribute reduction and rule generation: A case of patients with suspected breast cancer", Journal of the American Society for Information Science and Technology , vol. 55, issue 11, pp. 954-962 , 2004. Abstract
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Rough set theory is a relatively new intelligent technique used in the discovery of data dependencies; it evaluates the importance of attributes, discovers the patterns of data, reduces all redundant objects and attributes, and seeks the minimum subset of attributes. Moreover, it is being used for the extraction of rules from databases. In this paper, we present a rough set approach to attribute reduction and generation of classification rules from a set of medical datasets. For this purpose, we first introduce a rough set reduction technique to find all reducts of the data that contain the minimal subset of attributes associated with a class label for classification. To evaluate the validity of the rules based on the approximation quality of the attributes, we introduce a statistical test to evaluate the significance of the rules. Experimental results from applying the rough set approach to the set of data samples are given and evaluated. In addition, the rough set classification accuracy is also compared to the well-known ID3 classifier algorithm. The study showed that the theory of rough sets is a useful tool for inductive learning and a valuable aid for building expert systems.

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