Download Applications of Supervised and Unsupervised Ensemble Methods by Grigorios Tsoumakas, Ioannis Partalas (auth.), Oleg Okun, PDF

By Grigorios Tsoumakas, Ioannis Partalas (auth.), Oleg Okun, Giorgio Valentini (eds.)

ISBN-10: 3642039987

ISBN-13: 9783642039980

ISBN-10: 3642039995

ISBN-13: 9783642039997

This publication comprises the prolonged papers awarded on the 2d Workshop on Supervised and Unsupervised Ensemble equipment and their functions (SUEMA) hung on 21-22 July, 2008 in Patras, Greece, along side the 18th ecu convention on synthetic Intelligence (ECAI’2008). This workshop was once a successor of the smaller occasion held in 2007 along with third Iberian convention on development popularity and snapshot research, Girona, Spain. The luck of that occasion in addition to the book of workshop papers within the edited ebook “Supervised and Unsupervised Ensemble equipment and their Applications”, released by means of Springer-Verlag in stories in Computational Intelligence sequence in quantity 126, inspired us to proceed a very good tradition.

The function of this ebook is to aid practitioners in a number of branches of technological know-how and know-how to undertake the ensemble process for his or her day-by-day study paintings. we are hoping that fourteen chapters composing the ebook should be a superb consultant within the sea of various possibilities for ensemble methods.

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Pruning adaptive boosting. H. ) Proc. 14th Int. Conf. Mach. , Nashville, TN, pp. 211–218. Morgan Kaufmann, San Francisco (1997) 17. : Aggregation ordering in bagging. In: Proc. IASTED Int. Conf. Artif. Intell. , pp. 258–263. Acta Press, Calgari (2004) 18. : Pruning in ordered bagging ensembles. , Moore, A. ) Proc. 23rd Int. Conf. Mach. , Pittsburgh, PA, pp. 609– 616. ACM, New York (2006) 19. : Using boosting to prune bagging ensembles. Pattern Recogn. Lett. 28(1), 156–165 (2007) An Ensemble Pruning Primer 13 20.

Artif. , Patras, Greece, pp. 117– 121. IOS Press, Amsterdam (2008) 22. : Pruning an ensemble of classifiers via reinforcement learning. Neurocomputing (in press, 2009) 23. : Ensemble pruning using reinforcement learning. In: Proc. 4th Hellenic Conf. Artif. , Heraclion, Greece, pp. 301–310 (2006) 24. : Engineering multiversion neural-net systems. Neural Computation 8(4), 869–893 (1996) 25. : The strength of weak learnability. Mach. Learn. 5(2), 197–227 (1990) 26. : A cluster analysis method for grouping means in the analysis of variance.

4) is similar, with the obvious difference that the expected utility of the adversary is higher in the graphs of Fig. 4 than in Fig. 3, since it can afford a higher cost to modify instances. The opposite happens for the classifier. These experimental results on a real case study give thus a quantitative confirmation to the theoretical explanation given in Sect. 1 on the effectiveness of adding new classifiers based on different features in improving both the detection capability and the hardness of evasion of a security system like a spam filter.

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