Name
Abstract | ||
|---|---|---|
An integrated approach of knowledge discovery has been proposed in the paper using Rough Set Theory (RST) and Dempster-Shafer's (D-S) theory where high dimensional data is reduced in two folds. Firstly, unimportant attributes are eliminated using RST generating minimal subset of attributes, called reducts. Considering each core attribute as root of a decision tree, classification rules are built and grouped based on some similarity measure. Representative of each group constitute the new rule set and thus rules has been reduced while important information are retained. D-S theory ensembles the rules from which a classifier with highest accuracy has been selected. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1504/IJAISC.2010.038643 | IJAISC |
Keywords | Field | DocType |
efficient classifier design,highest accuracy,core attribute,integrated approach,knowledge discovery,rough set theory,d-s theory ensemble,decision tree,high dimensional data,dempster-shafer theory,important information,classification rule,core,data analysis,reduct,decision trees,dempster shafer theory,rough sets | Data mining,Decision tree,Reduct,Similarity measure,Computer science,Artificial intelligence,Data classification,Classifier (linguistics),Dominance-based rough set approach,Pattern recognition,Rough set,Dempster–Shafer theory,Machine learning | Journal |
Volume | Issue | Citations |
2 | 3 | 0 |
PageRank | References | Authors |
0.34 | 18 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Asit Kumar Das | 1 | 73 | 16.06 |
| Jaya Sil | 2 | 144 | 26.92 |