A rough set approach for the discovery of classification rules in interval-valued information systems

A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects optimal decision rules with a minimal set of features necessary and sufficient for classification of real-valued data. A rough set knowledge discovery framework is formulated for the analysis of interval-valued information systems converted from real-valued raw decision tables. The optimal feature selection method for information systems with interval-valued features obtains all classification rules hidden in a system through a knowledge induction process. Numerical examples are employed to substantiate the conceptual arguments

Ranking for Objects and Attribute Reductions in Intuitionistic Fuzzy Ordered Information Systems

We aim to investigate intuitionistic fuzzy ordered information systems. The concept of intuitionistic fuzzy ordered information systems is proposed firstly by introducing an intuitionistic fuzzy relation to ordered information systems. And a ranking approach for all objects is constructed in this system. In order to simplify knowledge representation, it is necessary to reduce some dispensable attributes in the system. Theories of rough set are investigated in intuitionistic fuzzy ordered information systems by defining two approximation operators. Moreover, judgement theorems and methods of attribute reduction are discussed based on discernibility matrix in the systems, and an illustrative example is employed to show its validity. These results will be helpful for decisionmaking analysis in intuitionistic fuzzy ordered information systems

Intuitionistic Fuzzy Soft Rough Set and Its Application in Decision Making

The soft set theory, originally proposed by Molodtsov, can be used as a general mathematical tool for dealing with uncertainty. In this paper, we present concepts of soft rough intuitionistic fuzzy sets and intuitionistic fuzzy soft rough sets, and investigate some properties of soft rough intuitionistic fuzzy sets and intuitionistic fuzzy soft rough sets in detail. Furthermore, classical representations of intuitionistic fuzzy soft rough approximation operators are presented. Finally, we develop an approach to intuitionistic fuzzy soft rough sets based on decision making and a numerical example is provided to illustrate the developed approach

Finding patterns in student and medical office data using rough sets

Data have been obtained from King Khaled General Hospital in Saudi Arabia. In this project, I am trying to discover patterns in these data by using implemented algorithms in an experimental tool, called Rough Set Graphic User Interface (RSGUI). Several algorithms are available in RSGUI, each of which is based in Rough Set theory. My objective is to find short meaningful predictive rules. First, we need to find a minimum set of attributes that fully characterize the data. Some of the rules generated from this minimum set will be obvious, and therefore uninteresting. Others will be surprising, and therefore interesting. Usual measures of strength of a rule, such as length of the rule, certainty and coverage were considered. In addition, a measure of interestingness of the rules has been developed based on questionnaires administered to human subjects. There were bugs in the RSGUI java codes and one algorithm in particular, Inductive Learning Algorithm (ILA) missed some cases that were subsequently resolved in ILA2 but not updated in RSGUI. I solved the ILA issue on RSGUI. So now ILA on RSGUI is running well and gives good results for all cases encountered in the hospital administration and student records data.Master's These