Evolving Ensembles
نویسندگان
چکیده
منابع مشابه
EVOLVING NEURAL NETWORKS ENSEMBLES NNEs
A new method to design and evolve neural network ensembles NNEs based on speciation is presented in this paper. The main advantage of this method is that, it completely evolves NNEs by combining the evolution of neural networks and the configuration of the ensemble in one evolutionary phase. In every generation, population is evolved toward the best set of structure and weights. Then, the ensem...
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XCS with computed prediction, namely XCSF, extends XCS by replacing the classifier prediction with a parametrized prediction function. Although several types of prediction functions have been introduced, so far XCSF models are still limited to evolving classifiers with the same prediction function. In this paper, we introduce XCSF with heterogeneous predictors, XCSFHP, which allows the evolutio...
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Classification systems are often designed using a limited amount of data from complex and changing pattern recognition environments. In applications where new reference samples become available over time, adaptive multi-classifier systems (AMCSs) are desirable for updating class models. In this paper, an incremental learning strategy based on an aggregated dynamical niching particle swarm optim...
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Ensembles of learning machines have been formally and empirically shown to outperform (generalise better than) single predictors in many cases. Evidence suggests that ensembles generalise better when they constitute members which form a diverse and accurate set. Additionally, there have been a multitude of theories on how one can enforce diversity within a combined predictor setup. We recently ...
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A Genetic Programming based boosting ensemble method for the classification of distributed streaming data is proposed. The approach handles flows of data coming from multiple locations by building a global model obtained by the aggregation of the local models coming from each node. A main characteristics of the algorithm presented is its adaptability in presence of concept drift. Changes in dat...
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ژورنال
عنوان ژورنال: Monthly Weather Review
سال: 2015
ISSN: 0027-0644,1520-0493
DOI: 10.1175/mwr-d-14-00058.1