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A Dynamic Integration Algorithm for an Ensemble of Classifiers

A Dynamic Integration Algorithm for an Ensemble of Classifiers,10.1007/BFb0095148,Seppo Puuronen,Vagan Y. Terziyan,Alexey Tsymbal

A Dynamic Integration Algorithm for an Ensemble of Classifiers   (Citations: 27)
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Numerous data mining methods have recently been developed, and there is often a need to select the most appropriate data mining method or methods. The method selection can be done statically or dynamically. Dynamic selection takes into account characteristics of a new instance and usually results in higher classification accuracy. We discuss a dynamic integration algorithm for an ensemble of classifiers. Our algorithm is a new variation of the stacked generalization method and is based on the basic assumption that each basic classifier is best inside certain subareas of the application domain. The algorithm includes two main phases: a learning phase, which collects information about the quality of classifications made by the basic classifiers into a performance matrix, and an application phase, which predicts the goodness of classification for a new instance produced by the basic classifiers using the performance matrix. In this paper we present also experiments made on three machine learning data sets, which show promising results.
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    • ...The dynamic selection (DS) and dynamic voting (DV) approaches in [11], [13] are in the same spirit as [5], [10]...

    Fotini Markatopoulouet al. Instance-Based Ensemble Pruning via Multi-Label Classification

    • ...Mixture of Experts [7] and Dynamic Integration [8], the combination method is changed according to the characteristics of testing samples and base classifiers in each classification phase...
    • ...Many studies showed that dynamic fusion methods outperform static fusion method [4, 8-10]...
    • ...Usually, the performance on the training samples or validation samples of testing sample is calculated [3, 8, 10]...

    Daniel S. Yeunget al. A Novel Dynamic Fusion Method Using Localized Generalization Error Mod...

    • ...When more than one model is selected, their results are combined (task 4). The dynamic voting (weighting) methods [22,29,24] obtain the final prediction by a 194 J. Mendes-Moreira et al...
    • ...2: it uses the vector w of weights (appendix B presents how w is calculated), and the matrix sqe with the squared error of the models for each similar data point, in order to obtain the weight for each model [22,24] according to:...

    João Mendes-moreiraet al. Ensemble Learning: A Study on Different Variants of the Dynamic Select...

    • ...We consider in our experiments three dynamic integration techniques based on the same local accuracy estimates: Dynamic Selection (DS) [13], Dynamic Voting (DV) [13], and Dynamic Voting with Selection (DVS) [19]...
    • ...We consider in our experiments three dynamic integration techniques based on the same local accuracy estimates: Dynamic Selection (DS) [13], Dynamic Voting (DV) [13], and Dynamic Voting with Selection (DVS) [19]...
    • ...Heterogeneous Euclidean-Overlap Metric (HEOM) [13] was used for calculation of the distances (for numeric features, the distance is calculated using the Euclidean metric, and for categorical features the simple 0/1 overlap metric is used)...

    Alexey Tsymbalet al. Diversity in Random Subspacing Ensembles

    • ...F(y1, …, yS): (1) the combination approach, where the base classifiers produce their classifications and the final result is composed of these; and (2) the selection approach, where one of the classifiers is selected and the final result is the result produced by it. For both approaches, there are static and dynamic methods [10]...
    • ...In our experiments we use five different integration methods: (1) cross-validation majority (a static selection approach, SS) [8]; (2) weighted voting (WV) [2] (a static combination approach); (3) dynamic selection (DS) [10] (a dynamic selection approach); (4) dynamic voting (DV) [10] (a dynamic combination approach); and (5) dynamic voting with selection (DVS) [12] (a dynamic hybrid approach)...
    • ...In our experiments we use five different integration methods: (1) cross-validation majority (a static selection approach, SS) [8]; (2) weighted voting (WV) [2] (a static combination approach); (3) dynamic selection (DS) [10] (a dynamic selection approach); (4) dynamic voting (DV) [10] (a dynamic combination approach); and (5) dynamic voting with selection (DVS) [12] (a dynamic hybrid approach)...

    Alexey Tsymbalet al. Search Strategies for Ensemble Feature Selection in Medical Diagnostic...

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