Establish a discern model of under skewed class distribution

Cien Yun Dai, Chi Jun Lu, Chiu Huin Liao, Chen Chieh Yeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Under Skewed Class Distribution Data (SCDD) Real-world applications often involve highly skewed data in decision outcomes. Such a highly skewed class distribution problem, if not properly addressed, would imperil the resulting learning effectiveness. We use one hospital Central Venous Catheter Insertion data for the training data. Among them find CVP-tip attribute are skewed class distribution. The paper uses under-sampling over-sampling and cost-sensitivity to solve SCDD problem. And then use C4.5 algorithms to build up classification model for the central venous catheter insertion. Verify via the expert that use this model can effectively help the medical expert to extract out it about Central Venous Catheter Insertion good knowledge rules.

Original languageEnglish
Title of host publicationIMECS 2007 - International MultiConference of Engineers and Computer Scientists 2007
Pages801-804
Number of pages4
Publication statusPublished - 2007 Dec 1
EventInternational MultiConference of Engineers and Computer Scientists 2007, IMECS 2007 - Kowloon, Hong Kong
Duration: 2007 Mar 212007 Mar 23

Publication series

NameLecture Notes in Engineering and Computer Science
ISSN (Print)2078-0958

Other

OtherInternational MultiConference of Engineers and Computer Scientists 2007, IMECS 2007
CountryHong Kong
CityKowloon
Period07/3/2107/3/23

Keywords

  • Cost-sensitivity C4.5 algorithms
  • Over-sampling
  • Under-sampling

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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