By Thomas G. Dietterich (auth.)
This e-book constitutes the refereed court cases of the 1st overseas Workshop on a number of Classifier platforms, MCS 2000, held in Cagliari, Italy in June 2000.
The 33 revised complete papers awarded including 5 invited papers have been rigorously reviewed and chosen for inclusion within the ebook. The papers are equipped in topical sections on theoretical concerns, a number of classifier fusion, bagging and boosting, layout of a number of classifier platforms, purposes of a number of classifier platforms, record research, and miscellaneous functions.
Read Online or Download Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21–23, 2000 Proceedings PDF
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Extra resources for Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21–23, 2000 Proceedings
Demuth and M. Beale, Neural Network TOOLBOX for use with Matlab, version 3 Mathworks, Natick, MA, USA, 1998. C. Sharkey, Noel E. O. Chandroth Department of Computer Science, University of Sheﬃeld, UK Abstract. The performance of neural nets can be improved through the use of ensembles of redundant nets. In this paper, some of the available methods of ensemble creation are reviewed and the “test and select” methodolology for ensemble creation is considered. This approach involves testing potential ensemble combinations on a validation set, and selecting the best performing ensemble on this basis, which is then tested on a ﬁnal test set.
This may be interesting as this method is fast, in training as well as in testing. This good performance of combining classifiers trained on randomly selected feature sets corresponds with the use of random subspaces in combining weak classifiers. The results of the 1-NN and Parzen combinations are quite acceptable, but are not as good as in the original feature sets. Probably these classifiers suffer from the fact that distances within one feature set are not very well defined (by the differences in scale in the original feature sets, which are now mixed).
The process continues for the speciﬁed number of rounds. Ensembles created through Adaboost have been shown to produce good results when compared to Bagging on a number of data sets (). Breiman suggests that bagging, and some of the methods described above as ”distortion” (namely randomising the construction of predictors, and randomising the outputs) are essentially ”cut from the same cloth”, and that there is something fundamentally diﬀerent about the adaptive resampling involved in Adaboost, and similar algorithms he terms ”arcing” algorithms (Adaptive Reweighting and Combining).