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

  1. Get@NRC: A learning method for developing PROAFTN classifiers and a comparative study with decision trees (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1007/978-3-642-21043-3_7
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TypeBook Chapter
Proceedings titleAdvances in Artificial Intelligence : 24th Canadian Conference on Artificial Intelligence, Canadian AI 2011, St. John’s, Canada, May 25-27, 2011. Proceedings
Series titleLecture Notes In Computer Science; Volume 6657
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
Pages5661; # of pages: 6
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
AbstractPROAFTN belongs to Multiple-Criteria Decision Aid (MCDA) paradigm and requires a several set of parameters for the purpose of classification. This study proposes a new inductive approach for obtaining these parameters from data. To evaluate the performance of developed learning approach, a comparative study between PROAFTN and a decision tree in terms of their learning methodology, classification accuracy, and interpretability is investigated in this paper. The major distinguished property of Decision tree is that its ability to generate classification models that can be easily explained. The PROAFTN method has also this capability, therefore avoiding a black box situation. Furthermore, according to the proposed learning approach in this study, the experimental results show that PROAFTN strongly competes with ID3 and C4.5 in terms of classification accuracy. © Her Majesty the Queen in Right of Canada 2011.
Publication date
PublisherSpringer Berlin Heidelberg
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology
Peer reviewedYes
NPARC number21271550
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Record identifierfccb9c41-0599-44ee-9eed-6845c173667a
Record created2014-03-24
Record modified2016-06-21
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