A learning method for developing PROAFTN classifiers and a comparative study with decision trees

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1007/978-3-642-21043-3_7
AuthorSearch for: 1; Search for: 1
Affiliation
  1. National Research Council of Canada. NRC Institute for Information Technology
FormatText, Book Chapter
Conference24th Canadian Conference on Artificial Intelligence, (AI 2011), Collocated with the 37th Graphics Interface Conference, (GI 2011) and 8th Canadian Conference on Computer and Robot Vision, (CRV 2011), May 25-27, 2011, St. John's, NL, Canada
Subjectblack boxes; classification; classification accuracy; classification models; comparative studies; interpretability; knowledge discovery; learning approach; learning methods; MCDA; multiple criteria decision aid; PROAFTN; artificial intelligence; computer vision; decision support systems; intelligent robots; interfaces (computer); plant extracts; decision trees
Abstract
Publication date
PublisherSpringer Berlin Heidelberg
In
Series
LanguageEnglish
Peer reviewedYes
NPARC number21271550
Export citationExport as RIS
Report a correctionReport a correction (opens in a new tab)
Record identifierfccb9c41-0599-44ee-9eed-6845c173667a
Record created2014-03-24
Record modified2020-03-03
Date modified: