Titlebook: Advances in Artificial Intelligence; 26th Canadian Confer Osmar R. Za?ane,Sandra Zilles Conference proceedings 2013 Springer-Verlag Berlin

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Cost-Sensitive Boosting Algorithms for Imbalanced Multi-instance Datasets978-3-642-76421-9

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0302-9743 adian Conference on Artificial Intelligence, Canadian AI 2012, held in Regina, SK, Canada, in May 2013. The 17 regular papers and 15 short papers presented were carefully reviewed and selected from 73 initial submissions and are accompanied by 8 papers from the Graduate Student Symposium that were s

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Marketingkommunikation im digitalen Wandelhe simplification of the uncertain decision table to generate more significant attributes is based on computing all possible reducts. To obtain these reducts, we propose a new definition of the concepts of discernibility matrix and function under the belief function framework. Experimentations have been done to evaluate this exhaustive solution.

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Theoretische und konzeptionelle Grundlagen, to use with duplicate detection and discusses circumstances in which each of these conditions might not hold, i.e. circumstances in which it would be safe to use move pruning in conjunction with duplicate detection.

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Brigitte Gaiser,Richard Linxweileri-instance classifier learning algorithms. Cost-sensitive boosting algorithms are developed by introducing cost items into the learning framework of AdaBoost, to enable classification of imbalanced multi-instance datasets.

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