By Aysha Akther, Heung-Nam Kim, Majdi Rawashdeh, Abdulmotaleb El Saddik (auth.), Leila Kosseim, Diana Inkpen (eds.)
This ebook constitutes the refereed lawsuits of the twenty fifth Canadian convention on man made Intelligence, Canadian AI 2012, held in Toronto, Canada, in may possibly 2012. The 23 average papers, sixteen brief papers, and four papers from the Graduate pupil Symposium offered have been conscientiously reviewed and chosen for inclusion during this ebook. The papers conceal a extensive variety of issues proposing unique paintings in all parts of synthetic 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
DIR, BOA, and PERF), we set the cut-oﬀ CPU time to one hour and use that value as the recorded run-time of timed-out instances. For ParamILS learning, the time to generate a macro set for a given cluster of instances was varied depending on the number of initial macros and the diﬃculty of the original domain. For most experiments the cut-oﬀ time was one or two hours. , the mystery-VHPOP-MANUAL cut-oﬀ was four hours). Online, one CPU hour is given to solve each test instance. The run-time of timed-out instances is recorded as one hour and the test instances for which all macro sets timed out are not considered in the evaluation.
Given this problem, some authors tried to include extra information to the binary classiﬁers of the one-versus-one scheme in the ECOC framework without retraining the binary problems . The main idea of  is to include information about classes not included in each pairwise partition by using the confusion in the training data. However, performance improvement is not always guarantee, and the computationally complexity of the method is slightly Eﬃcient Pairwise Classiﬁcation Using Local Cross Oﬀ Strategy 29 increased.
We compared our proposed method with OVO, OVA, A&O, and ECOC methods on 20 multiclass datasets from the UCI machine learning repository , which are summarized in Table 1. The class of an instance in the ECOC schemes is chosen using the Hamming distance. As mentioned before, a modiﬁed k-nearest neighbor algorithm was chosen to determine the most probable classes. Based on a preliminary set of experiments, the value of k was set to K = 5. The next decision was which base classiﬁer to use. In this study, two base learners were chosen: Support Vector Machines (SVMs) using linear kernel and a Multilayer Perceptron (MLP).
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.)