Cost Sensitive Decision Forest and Voting for Software Defect Prediction

Michael Siers, Md Zahidul Islam

Research output: Book chapter/Published conference paperConference paper

6 Citations (Scopus)

Abstract

While traditional classification algorithms optimize for accuracy, cost-sensitive classification methods attempt to make predictions that produce the lowest classification cost. In this paper we propose a cost-sensitive classification technique called CSForest which is an ensemble of decision trees. We also propose a cost-sensitive voting technique called CSVoting. The proposed techniques are empirically evaluated by comparing them with five (5) classifier algorithms on six (6) publicly available clean datasets that are commonly used in the research on software defect prediction. Our initial experimental results indicate a clear superiority of the proposed techniques over the existing ones.
Original languageEnglish
Title of host publicationPRICAI 2014
Place of PublicationGermany
PublisherSpringer-Verlag London Ltd.
Pages929-936
Number of pages8
Volume8862
DOIs
Publication statusPublished - 2014
EventPacific Rim International Conference on Artificial Intelligence - Gold Coast, Australia
Duration: 01 Dec 201405 Dec 2014

Publication series

Name
ISSN (Print)0302-9743

Conference

ConferencePacific Rim International Conference on Artificial Intelligence
CountryAustralia
Period01/12/1405/12/14

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  • Cite this

    Siers, M., & Islam, M. Z. (2014). Cost Sensitive Decision Forest and Voting for Software Defect Prediction. In PRICAI 2014 (Vol. 8862, pp. 929-936). Springer-Verlag London Ltd.. https://doi.org/10.1007/978-3-319-13560-1