Merz, C.J., Murphy, P.M.: UCI Repository of Machine Learning Datasets [http://www.ics.uci.edu/ (mlearn/MLRepository.html]. Dep-t of Information and CS, Un-ty of California, Irvine, CA (1998) Home/Search Document Not in Database Summary Related Articles Check |
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Ensemble Feature Selection with Dynamic Integration of.. - Tsymbal, Puuronen.. (2001) (Correct)
....voting with selection (DVS) The dynamic integration of classifiers (DS, DV, and DVS) is based on the assumption that each base classifier is best inside certain subareas of the whole instance space. We conduct experiments on seven multi class data sets from the UCI machine learning repository [8]. The experimental results demonstrate benefits of the dynamic integration methods over the static ones. Section 2 discusses feature selection and especially the chosen heuristic. In Section 3 we describe integration of classifiers and especially the dynamic classifier integration. In the next ....
....of the use of cross validation in the evaluation of the base classifiers. And finally, the number of nearest neighbors in dynamic integration is analyzed. 4. 1 Experimental setting Seven data sets including instances of more than two classes were selected from the UCI machine learning repository [8]. The main characteristics of those data sets are presented in Table 1. For each data set used the table provides the name of the data set, the number of instances, the number of classes, and the numbers of different kinds of features. Table 1. Data sets used in the experiments Features Dataset ....
Merz, C.J., Murphy, P.M.: UCI Repository of Machine Learning Datasets [http://www.ics.uci.edu/ ~mlearn/MLRepository.html]. Dep-t of Information and CS, Un-ty of California, Irvine, CA (1998).
Correlation-Based and Contextual Merit-Based Ensemble.. - Puuronen, Tsymbal.. (Correct)
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Merz, C.J., Murphy, P.M.: UCI Repository of Machine Learning Datasets [http://www.ics.uci.edu/ (mlearn/MLRepository.html]. Dep-t of Information and CS, Un-ty of California, Irvine, CA (1998)
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