FTXI: Fault tolerance XCS in integer

Hong Wei Chen*, Ying-Ping Chen

*Corresponding author for this work

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


In the realm of data mining, several key issues exists in the traditional classification algorithms, such as low readability, large rule number, and low accuracy with information losing. In this paper, we propose a new classification methodology, called fault tolerance XCS in integer (FTXI), by extending XCS to handle conditions in integers and integrating the mechanism of fault tolerance in the context; of data mining into the framework of XCS. We also design and generate appropriate artificial data sets for examining and verifying the proposed method. Our experiments indicate that FTXI can provide the least rule number, obtain high prediction accuracy, and offer rule readability, compared to C4.5 and XCS in integer without fault tolerance.

Original languageEnglish
Title of host publicationGECCO 2006 - Genetic and Evolutionary Computation Conference
Number of pages2
StatePublished - 30 Oct 2006
Event8th Annual Genetic and Evolutionary Computation Conference 2006 - Seattle, WA, United States
Duration: 8 Jul 200612 Jul 2006

Publication series

NameGECCO 2006 - Genetic and Evolutionary Computation Conference


Conference8th Annual Genetic and Evolutionary Computation Conference 2006
CountryUnited States
CitySeattle, WA


  • Classification
  • Data mining
  • Fault tolerance
  • Integer representation
  • XCS

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