A rapid growth of interest in rough set theory [297] and its applications can be lately seen in
the number of international workshops, conferences and seminars that are either directly …

The main statistics used in rough set data analysis, the approximation quality, is of limited
value when there is a choice of competing models for predicting a decision variable. In …

A new methodology of extraction, optimization, and application of sets of logical rules is
described. Neural networks are used for initial rule extraction, local or global minimization …

In many applications, black-box prediction is not satisfactory, and understanding the data is
of critical importance. Typically, approaches useful for understanding of data involve logical …

This paper presents a machine learning approach to characterizing premonitory factors of
earthquake. The characteristic asymmetric distribution of seismic events and sampling …

Rough set theory has been proposed by Pawlak as a tool for dealing with the vagueness
and granularity in information systems. The core concepts of classical rough sets are lower …

Rough set theory (RST) offers an interesting and novel approach both to the generation of
rules for use in expert systems and to the traditional statistical task of classification. The …

Various techniques for the extraction of ANN rules have been used, but most of them have
focused on certain types of networks and their training. There are very few methods that deal …

Rough set data analysis (RSDA), introduced by Pawlak, has become a much researched
method of knowledge discovery with over 1200 publications to date. One feature which …

We present an algorithm to impute missingvalues from given dataalone, and analyse its
performance. Theproposed procedure is based onnon-numeric rule based data analysis …