DOI | Resolve DOI: https://doi.org/10.1007/978-3-642-21043-3_7 |
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Author | Search for: Belacel, N.1; Search for: Al-Obeidat, F.1 |
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Affiliation | - National Research Council of Canada. NRC Institute for Information Technology
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Format | Text, Book Chapter |
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Conference | 24th 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 |
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Subject | black 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 |
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Abstract | PROAFTN 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. |
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Publication date | 2011 |
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Publisher | Springer Berlin Heidelberg |
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Series | |
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Language | English |
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Peer reviewed | Yes |
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NPARC number | 21271550 |
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Export citation | Export as RIS |
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Report a correction | Report a correction (opens in a new tab) |
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Record identifier | fccb9c41-0599-44ee-9eed-6845c173667a |
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Record created | 2014-03-24 |
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Record modified | 2020-03-03 |
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