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Aysha Akther, Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb's Advances in Artificial Intelligence: 25th Canadian PDF

By Aysha Akther, Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb El Saddik (auth.), Leila Kosseim, Diana Inkpen (eds.)

ISBN-10: 3642303536

ISBN-13: 9783642303531

This e-book constitutes the refereed lawsuits of the twenty fifth Canadian convention on man made Intelligence, Canadian AI 2012, held in Toronto, Canada, in might 2012. The 23 commonplace papers, sixteen brief papers, and four papers from the Graduate scholar Symposium offered have been rigorously reviewed and chosen for inclusion during this ebook. The papers hide a huge variety of issues featuring unique paintings in all components of man-made intelligence, both theoretical or applied.

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Additional info for Advances in Artificial Intelligence: 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Toronto, ON, Canada, May 28-30, 2012. Proceedings

Example text

Ties are usually broken arbitrarily for the larger class. More complicated combination methods have also been proposed [22] [26]. Ko and Byun proposed a method based on combination of the one-versus-all method and a modification of the one-versus-one method using SVM as a base learner [19]. This method first obtain the top two classes whose corresponding classifiers have the highest confident based on the outputs of all one-versus-all classifiers. In a recent paper [12], very similar idea is presented and named A&O.

Gao, and S. Escalera Experimental Result Analysis As can be seen in Tables 2-5, the proposed approach is generally able to outperform all the other methods for the two types of base learners. As a general conclusion, the advanced performance of the LCO method does not differ much depending on the base classifier. 06% for MLP and linear SVM, respectively. The other finding is that the one-versus-all method generally performs poorly in the present experiments, especially for MLP neural network. An analysis of the results shows a somewhat clearer picture.

Pedrajas and Boyer’s prominent paper [13] later presented an in-depth critical assessment of the three basic multiclass methods. One of the main paper’s conclusions states that ECOC and one-versus-one are the best choices for powerful learners and for simpler learners, respectively. However, in the ECOC approach, since the ensemble system usually has more individual classifiers than one-versus-one and one-versus-all, it usually requires more computation, especially for the training phase. Regarding the one-versus-one method, as mentioned before, many of the binary base classifiers are forced to give wrong votes for a given test pattern.

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Advances in Artificial Intelligence: 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Toronto, ON, Canada, May 28-30, 2012. Proceedings by Aysha Akther, Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb El Saddik (auth.), Leila Kosseim, Diana Inkpen (eds.)


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