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 includes the prolonged papers offered on the 2d Workshop on Supervised and Unsupervised Ensemble tools and their purposes (SUEMA) hung on 21-22 July, 2008 in Patras, Greece, along with the 18th ecu convention on synthetic Intelligence (ECAI’2008). This workshop was once a successor of the smaller occasion held in 2007 together with third Iberian convention on development acceptance and photo research, Girona, Spain. The good fortune of that occasion in addition to the book of workshop papers within the edited publication “Supervised and Unsupervised Ensemble tools and their Applications”, released by means of Springer-Verlag in stories in Computational Intelligence sequence in quantity 126, inspired us to proceed an excellent tradition.

The function of this publication is to aid practitioners in a variety of branches of technological know-how and know-how to undertake the ensemble strategy for his or her day-by-day examine paintings. we are hoping that fourteen chapters composing the e-book could be an outstanding consultant within the sea of various possibilities for ensemble methods.

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Extra resources for Applications of Supervised and Unsupervised Ensemble Methods

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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.

This is a clear evidence that adding new classifiers based on different features can allow to improve not only the classifier’s discriminant capability, but also its hardness of evasion. Consider finally the bottom-right graph, corresponding to the case when the classifier adds new tests, and the adversary uses the strategy which was optimal against the previous set of tests. For smaller number of tests (up to 319), the expected utility of the adversary is between the ones of the first two graphs: this is reasonable, because it is now trying to evade only some of the tests used by the classifier.

3] for adversarial classification problems. Then, to experimentally investigate the above issues we consider a spam filtering task as a case study, using a large corpus of publicly available spam and legitimate e-mails, and a real and widely used open source spam filter, SpamAssassin. The chapter is organised as follows. In Sect. 1 we give an overview of the use of MCSs in security applications, and of the theoretical framework proposed in [3] for adversarial classification problems. In Sect. 3 we describe how we model an adversarial learning task in which the classifier is a MCS using the framework in [3], and how we model such kinds of tasks to investigate the effectiveness of a MCS against a single classifier, when both classifiers use the same feature set.

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Applications of Supervised and Unsupervised Ensemble Methods by Grigorios Tsoumakas, Ioannis Partalas (auth.), Oleg Okun, Giorgio Valentini (eds.)


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