ProICET - A cost-sensitive system for the medical domain

Rodica Potolea, Camelia Vidrighin, Cristina Savin

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

Abstract

In recent years, data mining has started to receive increasing interest as a method of complementing domain specific expertise in various spheres of human activity. Apart from data specific issues, a key particularity of many real world problems, such as medical diagnosis, are the costs involved, the most important being the test and the misclassification costs. This paper evaluates ProICET, a new system built around the ICET algorithm. The system has been previously benchmarked on classical medical data sets. Here, we use a real medical dataset to test the current version of our system. The comparative analysis confirms that ProICET is the best at cost minimization out of several successful classifiers, while keeping a good accuracy rate.

Original languageEnglish (US)
Title of host publicationProceedings - Third International Conference on Natural Computation, ICNC 2007
Pages338-342
Number of pages5
DOIs
StatePublished - 2007
Event3rd International Conference on Natural Computation, ICNC 2007 - Haikou, Hainan, China
Duration: Aug 24 2007Aug 27 2007

Publication series

NameProceedings - Third International Conference on Natural Computation, ICNC 2007
Volume2

Other

Other3rd International Conference on Natural Computation, ICNC 2007
CountryChina
CityHaikou, Hainan
Period8/24/078/27/07

ASJC Scopus subject areas

  • Applied Mathematics
  • Computational Mathematics
  • Modeling and Simulation

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