On rough sets, their recent extensions and applications

Abstract

Rough set theory (RST) has enjoyed an enormous amount of attention in recent years and has been applied to many real-world problems including data mining, pattern recognition, and intelligent control. Much research has recently been carried out in respect of both the development of the underlying theory and the application to new problem domains. This paper attempts to summarize the advances in RST, its extensions, and their applications. It also identifies important areas which require further investigation. Typical example application domains are examined which demonstrate the success of the application of RST to a wide variety of areas and disciplines, and which also exhibit the strengths and limitations of the respective underlying approaches.

An Enhanced Rough Set based Feature Grouping Approach for Supervised Feature Selection

“…column1, column2, colum3 and column4 represents the dataset name, number of objects it contains, number of features and number of classes respectively. The classifier tool WEKA [10] is used for classification process. WEKA is an open source java based machine-learning workbench.…”

“…After the feature reduced set generated by the above mentioned algorithms, two classifier learners JRip and J48 are employed for the classification task. J48 is open source implementation of the C4.5 algorithm and JRip is a rule based classifier [10]. The average classification accuracy of individual classifier in terms of percentage is obtained by using 10-fold cross validation [11].…”

Attribute reduction based on improving DIT in interval‐valued ordered information system

“…Rough set theory was founded in the 1980s by Pawlak [1], a professor of Warsaw University of Technology and academician of the Polish Academy of Sciences. After more than 30 years of rapid development, a relatively complete theoretical system has been formed [2–7], and a very rich application research results have been achieved. In particular, compared with other methods, a rough set theory is more suitable for dealing with uncertainties contained in information systems.…”

A New Knowledge Characteristics Weighting Method Based on Rough Set and Knowledge Granulation

“…Rough set theory can be used to analyze and process the fuzzy or uncertain data without the a priori knowledge [ 11 – 17 ]. Now, the rough set theory has been widely used in pattern recognition [ 18 – 20 ], data mining [ 21 – 23 ], machine learning [ 24 – 29 ], and other fields [ 30 – 36 ].…”