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Diversity among base classifiers is known to be a necessary condition for improving ensemble learning performance. In this paper, methods of selective ensemble learning including hill-climbing selection, ensemble forward sequential selection, ensemble backward sequential selection and clustering selection are studied. To measure the diversity among base classifiers in ensemble learning, the entropy...
DBSCAN is a typical density-based clustering algorithm, but it is time-consuming to ascertain the parameter Eps and it does not perform well on multi-density datasets because of the global parameter Eps. In this paper, we use must-link constraints to ascertain the parameter Eps for each density distribution effectively and automatically, which will be used to deal with multi-density data sets for...
Cluster ensembles method is considered as a robust and accurate alternative to single clustering runs. It mainly consists of both generation of individual member and fusion methods. In this paper, we study the cluster ensembles where individual members are obtained based on k-means clustering algorithm and fusion method of hierarchical clustering is used. Three consensus functions, which are single...
There are many complicated data in real world, clustering analysis should be able to find the clusters of different shapes and densities. The existing typical clustering algorithms do not perform well on multi-density data. A semi-supervised clustering algorithm for multi-density dataset SCMD is proposed. The pairwise constraints: must-link and cannot-link that reflect the distribution of multi-density...
Aiming at diversity being a necessary condition of the ensemble learning, we study method for improving diversity of the neural networks ensemble based on K-means clustering technique. In this paper, we propose a selecting approach that is first to train many classifiers through training set with neural network algorithm, and to classify data on validation set using classifiers. And then we use the...
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