@INPROCEEDINGS { Rsds1425,
author = {Minz, Sonajharia and Jain, Rajni}, 
title = {Rough Set Based Decision Tree Model for Classification}, 
booktitle = {Data Warehousing and Knowledge Discovery}, 
conference = {Data Warehousing and Knowledge Discovery, International Conference}, 
series =  = {Lecture Notes in Computer Science}, 
volume = 2737, 
pages = {172-181}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
month = {September 3-5}, 
year = 2003, 
issn = {0302-9743}, 
isbn = {3-540-40807-X}, 
abstract = {Decision tree, a commonly used classification model, is constructed recursively following a top down approach (from the general concepts to particular examples) by repeatedly splitting the training data set. ID3 is a greedy algorithm that considers one attribute at a time for splitting at a node. In C4.5, all attributes, barring the nominal attributes used at the parent nodes, are retained for further computation. This leads to extra overheads of memory and computational efforts. Rough Set theory (RS) simplifies the search for dominant attributes in the information systems. In this paper, Rough set based Decision Tree (RDT) model combining the RS tools with classical DT capabilities, is proposed to address the issue of computational overheads. The experiments compare the performance of RDT with RS approach and ID3 algorithm. The performance of RDT over RS approach is observed better in accuracy and rule complexity while RDT and ID3 are comparable}, 
keywords = {rough set, supervised learning, decision tree, feature selection, classification, data mining, }, 
}