Read e-book online Multiple Classifier Systems: First International Workshop, PDF

By Thomas G. Dietterich (auth.)

ISBN-10: 3540677046

ISBN-13: 9783540677048

This e-book constitutes the refereed complaints of the 1st foreign Workshop on a number of Classifier structures, MCS 2000, held in Cagliari, Italy in June 2000.
The 33 revised complete papers provided including 5 invited papers have been conscientiously reviewed and chosen for inclusion within the e-book. 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, rfile research, and miscellaneous purposes.

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Extra resources for Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21–23, 2000 Proceedings

Example text

A preliminary report of that extension is provided here. The “Test and Select” Approach to Ensemble Combination 41 Table 4. Combining localisation SOMs Size of ensemble No. 89 11 1 96% N/A Considerable experimentation with SOM architectures and learning parameters was carried out before choosing the SOM used in the earlier study; it is therefore possible to look at the improvement that could be gained from combining localisation decisions based on different SOMs, in an ensemble. In total, 11 SOMs were considered.

E. sampling without replacement [12], or alternatively, using different cross-validation leave out sets [13]. The use of different data sources for training ensemble members is sometimes possible under circumstances in which data are available from more than one sensor. If the quality of data from each sensor is such that it is sufficient for the classification task (as opposed to poorer quality data where some form of sensor fusion is required before classification becomes possible), then an ensemble can be created from nets each trained on data from a separate sensor (see for instance [14], and the first case study described below).

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. The application of this methodology, and of ensembles in general, is explored further in two case studies. The first case study is of fault diagnosis in a diesel engine, and relies on ensembles of nets trained from three different data sources. The second case study is of robot localisation, using an evidence-shifting method based on the output of trained SOMs.

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Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21–23, 2000 Proceedings by Thomas G. Dietterich (auth.)

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