Rule based Model for Credit Evaluation using Rough Set Approach

Credit plays an important role in the economy. Credit Evaluation of any potential credit application has remained a challenge for banks all over the world till today. Credit evaluation can be defined as a technique that helps lenders to decide whether to grant credit to consumers or not. Its increasing importance can be seen from the growing popularity and application of credit scoring. It is mandatory not only to construct effective credit scoring models to help improve the bottom-line of credit providers, but also to design rule based system for effective credit evaluation system. This paper approaches the use of rough set technique to generate rule based system for credit scoring model

Attribute Reduction for Credit Evaluation using Rough Set Approach

Generation of an Integrated Model is an important technique in the research area. It is a powerful technique to improve the accuracy of classifiers. This approach has been applied to different types of real time data. The unprocessed data leads to give wrong results by using some of the machine learning techniques. For generation of an integrated model attribute reduction and re-sampling technique is necessary. For attribute reduction Rough set is the best approach as it requires less execution time, high Interpretability, high reduction rate and high accurac

Minimal Decision Rules Based on the A Priori Algorithm

Based on rough set theory many algorithms for rules extraction from data have been proposed. Decision rules can be obtained directly from a database. Some condition values may be unnecessary in a decision rule produced directly from the database. Such values can then be eliminated to create a more comprehensi- ble (minimal) rule. Most of the algorithms that have been proposed to calculate minimal rules are based on rough set theory or machine learning. In our ap- proach, in a post-processing stage, we apply the Apriori algorithm to reduce the decision rules obtained through rough sets. The set of dependencies thus obtained will help us discover irrelevant attribute values

The investigation of the Bayesian rough set model

AbstractThe original Rough Set model is concerned primarily with algebraic properties of approximately defined sets. The Variable Precision Rough Set (VPRS) model extends the basic rough set theory to incorporate probabilistic information. The article presents a non-parametric modification of the VPRS model called the Bayesian Rough Set (BRS) model, where the set approximations are defined by using the prior probability as a reference. Mathematical properties of BRS are investigated. It is shown that the quality of BRS models can be evaluated using probabilistic gain function, which is suitable for identification and elimination of redundant attributes

The investigation of the Bayesian rough set model

AbstractThe original Rough Set model is concerned primarily with algebraic properties of approximately defined sets. The Variable Precision Rough Set (VPRS) model extends the basic rough set theory to incorporate probabilistic information. The article presents a non-parametric modification of the VPRS model called the Bayesian Rough Set (BRS) model, where the set approximations are defined by using the prior probability as a reference. Mathematical properties of BRS are investigated. It is shown that the quality of BRS models can be evaluated using probabilistic gain function, which is suitable for identification and elimination of redundant attributes

Class Association Rules Mining based Rough Set Method

This paper investigates the mining of class association rules with rough set approach. In data mining, an association occurs between two set of elements when one element set happen together with another. A class association rule set (CARs) is a subset of association rules with classes specified as their consequences. We present an efficient algorithm for mining the finest class rule set inspired form Apriori algorithm, where the support and confidence are computed based on the elementary set of lower approximation included in the property of rough set theory. Our proposed approach has been shown very effective, where the rough set approach for class association discovery is much simpler than the classic association method.Comment: 10 pages, 2 figure