Special issue: Rough and Fuzzy Methods for Data Mining
Aboul Ella Hassanien, Hiroshi Sakai, Dominik Ślȩzak, Michir K. Chakraborty, William Zhu · International Journal of Hybrid Intelligent Systems · 2011
This special issue of International Journal of Hybrid Intelligent Systems (IJHIS) published by IOS Press contains a selection of papers presented initially at the RSFDGrC’09 Conference (Rough Sets, Fuzzy Sets, Data Mining and Granular Computing) held in IIT Delhi, India, on December 16–20, 2009. RSFDGrC is the series of international scientific conferences spanning over last 15 years. It investigates the meeting points among the four major areas outlined in its title, with respect to foundations and applications. Five papers included in this special issue are devoted to various aspects of rough sets, fuzzy sets, data mining and granular computing, with a special emphasis on hybrid methodologies for solving theoretical problems and dealing with practical challenges of representing and mining compound data. The first paper, “Facilitating Efficient Mars Terrain Image Classification with Fuzzy-Rough Feature Selection” by Changjing Shang, Dave Barnes and Qiang Shen, presents an application study of exploiting fuzzyrough feature selection (FRFS) techniques in aid of efficient and accurate Mars terrain image classification. The employment of FRFS allows for the induction of low-dimensionality feature sets from sample descriptions of feature vectors of a much higher dimensionality. Supported with comparative studies, the work demonstrates that FRFS helps to enhance both the ef-