A new decision tree construction using the cloud transform and rough sets

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Abstract

Many present methods for dealing with the continuous data and missing values in information systems for constructing decision tree do not perform well in practical applications. In this paper, a new algorithm, Decision Tree Construction based on the Cloud Transform and Rough Set Theory under Characteristic Relation (DTCCRSCR), is proposed for mining classification knowledge from the data set. The cloud transform is applied to discretize continuous data and the attribute whose weighted mean roughness under the characteristic relation is the smallest will be selected as the current splitting node. Experimental results show the decision trees constructed by DTCCRSCR tend to have a simpler structure, much higher classification accuracy and more understandable rules than C5.0 in most cases.

Details

Original languageEnglish
Title of host publicationRough Sets and Knowledge Technology
Place of PublicationHeidelberg, Germany
Pages524-531
Volume1
Publication statusPublished - May 2008
EventThird International Conference, RSKT 2008 - Chengdu, China
Duration: 17 May 200819 May 2008

Publication series

NameLecture Notes in Artificial Intelligence (LNAI) (5009)
NumberISSN 0302-9743

Conference

ConferenceThird International Conference, RSKT 2008
CountryChina
CityChengdu
Period2008-05-172008-05-19

Keywords

  • Rough sets, Cloud transform, Decision trees, Weighted mean roughness, Characteristic relation

ID: 105990