Limited tolerance relation-based decision tree algorithm

  • Ting Liang Wang*
  • , Li Wang
  • , Guo Ping Xia
  • , Ying Cheng Xu
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this research, we study how to generate a decision tree from dataset with unknown values, and proposed a decision tree learning algorithm (LTR-C4.5). The algorithm based on limited tolerance relation and C4.5. Algorithm LTRC4.5 is composed by two function modules: filling the unknown values and generating a decision tree. The algorithm recursive calls the two function modules when handling incomplete training samples. The outstanding feature of LTRC4.5 is that it doesn't demand to fill all unknown values before generating a decision tree. Some experiments are used to simulation the algorithm and compared it to other methods.

Original languageEnglish
Title of host publication6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
Pages221-226
Number of pages6
DOIs
StatePublished - 2009
Event6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009 - Tianjin, China
Duration: 14 Aug 200916 Aug 2009

Publication series

Name6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
Volume1

Conference

Conference6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
Country/TerritoryChina
CityTianjin
Period14/08/0916/08/09

Keywords

  • Decision tree
  • Limited tolerance relation
  • LTR-C4.5
  • Missing values

Fingerprint

Dive into the research topics of 'Limited tolerance relation-based decision tree algorithm'. Together they form a unique fingerprint.

Cite this