Resampling-based selective clustering ensembles.

This paper proposes a novel clustering ensembles method, termed as resampling-based selective clustering ensembles method. ... Experimental results on several real data sets demonstrate that resampling-based selective clustering ensembles method is often able to achieve a better solution when compared with traditional clustering ... We describes resampling-based selective clustering ensembles method in Section 3. ...

doi:10.1016/j.patrec.2008.10.007 fatcat:qaegew2wlzgkbhuiscfzk3pmlq

In order to improve the whole classification accuracy, we propose a novel classifier ensemble framework based on K-means and resampling technique (EKR). ... sub-cluster to be the same as that of the minority classes through resampling technology, after that each adjusted sub-cluster and the minority class are combined into several balanced subsets, the base ... Figure 3 . 3 The procedure of EKR(The ensemble framework based on K-means and resampling). Figure 3 . 3 The procedure of EKR (The ensemble framework based on K-means and resampling). ...

doi:10.3390/app10051684 fatcat:ys5kanprsjajhnfgx275nuty74

DOAJ

The comparison between the two resampling methods and observed ensemble statistics constructed by clustering similar meteorological conditions demonstrate that the WB reproduces several features related ... In this study, we propose a wavelet based resampling scheme (WB) and compare it to the traditional Fourier based phase randomization bootstrap (FB) approach within the context of the turbulence energy ... the cluster (or ensemble) statistics. ...

doi:10.1016/j.physd.2005.05.015 fatcat:gdwijxdvwjdxviaktpkpfzqlci

For an ensemble-based data assimilation system, this requires both an adequate metric that quantifies the distance between the observed atmospheric state and the states simulated by the ensemble members ... We, therefore, propose a combination of resampling, which accounts for simulated state space clustering, and nudging. ... Filtering/resampling based on clustering maintains representativeness of the EPS. ...

doi:10.1007/s00703-013-0291-3 fatcat:kgb3lqr4zrhypckemtapu6uhdu

A new criterion for clusters validation is proposed in the paper and based on the new cluster validation criterion a clustering ensmble framework is proposed. ... The empirical studies show promising results for the ensemble obtained using the proposed criterion comparing with the ensemble obtained using the standard clusters validation criterion. ... common base and ensemble methods. ...

doi:10.12785/ijlms/010103 fatcat:qqnhpmwkubb67luc4j3tg4ooke

A new criterion for clusters validation is proposed in the paper and based on the new cluster validation criterion a clustering ensmble framework is proposed. ... The empirical studies show promising results for the ensemble obtained using the proposed criterion comparing with the ensemble obtained using the standard clusters validation criterion. ... common base and ensemble methods. ...

doi:10.1007/978-3-642-27337-7_19 fatcat:tuuqxyncx5fytfezevqyyuu6r4

The clusters which satisfy a threshold of this measure are selected to participate in clustering ensemble. For combining the chosen clusters, a co-association based consensus function is applied. ... Since the Evidence Accumulation Clustering method cannot derive the co-association matrix from a subset of clusters, a new EAC based method which is called Extended EAC, EEAC, is applied for constructing ... The ensemble selection method is designed based on quality and diversity, the two factors that have been shown to influence cluster ensemble performance. ...

doi:10.1007/978-3-642-23960-1_14 fatcat:tijs5gmxyzek7kzgrkfbb6l4oi

Number of clusters is varying from 2 to 5. The outputs of ensemble members are aggregated using simple, weighted and median based averaging. ... It is shown that ensembles achieve better prediction results than the individual network. ... Other used algorithms for selecting ensemble components are: pruning algorithm to eliminate redundant classifiers [22] selective algorithm based on bias/variance decomposition [23] , genetic algorithm ...

doi:10.5937/fmet1701051j fatcat:2ls7dbd7mfggfkkcvw4gnwuweu

DOAJ

We propose a clustering-based undersampling strategy to be used in combination with ensemble classifiers. ... We applied several single and ensemble classifiers both to the original dataset and to data preprocessed by means of different resampling methods. ... Clustering-Based Random Undersampling The results of ensemble classifiers applied to imbalanced data can be improved by combining them with resampling strategies. ...

doi:10.3390/app9245287 fatcat:tbjcf6keozgrlchjmdb2p6gkna

DOAJ Multiple Versions

In this paper, we propose a heterogeneous ensemble learning model based on data distribution (HELMDD) to deal with imbalanced data in CCFD. ... Specifically, K-Means randomly select k samples as initial clusters. ... Therefore, by comparing the AUC score of multiple base classifiers and selecting the base classifier with the best AUC score to build an ensemble model, we can effectively improve prediction performance ...

doi:10.1155/2021/2531210 fatcat:cjwjrdq43fhcbhnnxhf5zclngi

DOAJ

The method, which is based on the idea of aggregating results obtained from an ensemble of randomly resampled data (where both samples and genes are resampled), introduces a way of tilting the procedure ... so that the ensemble includes minimal representation from less important areas of the gene predictor space. ... DISCUSSION We have introduced an ensemble method based on weighted resampling for unsupervised learning. ...

doi:10.1093/biostatistics/kxm017 pmid:17573363 fatcat:vw2qon7pbbg6pmbyz3abtvsl3y

Any cluster that satisfies a predefined threshold of the mentioned measure is selected to participate in an elite ensemble. ... Empirical studies show that our proposed approach outperforms other cluster ensemble approaches. ... Roth and Lange [22] presented a new algorithm for data clustering based on feature selection. In their method, the resampling based stability measure is used to set the algorithm parameters. ...

doi:10.3233/ida-140647 fatcat:vlh4dvvhjzaohexy4lfsbe42xe

Then we employ this criterion to select the more robust clusters in the final ensemble. ... We also propose a new method named Extended Evidence Accumulation Clustering, EEAC, to construct the matrix of similarity from these selected clusters. ... The ensemble selection method is designed based on quality and diversity, the two factors that have been shown to influence cluster ensemble performance. ...

doi:10.1007/978-3-642-25085-9_38 fatcat:l7ughgk2bvcyndekbzbq5dqo3u

If the clustering is stable, then the clusters from the original data will be preserved in the perturbed data clustering. ... Thus, understanding the quality of a clustering is of critical importance. The concept of stability has emerged as a strategy for assessing the performance and reproducibility of data clustering. ... Ensemble clustering accounts for uncertainty in the clustering technique selected, while stability estimation focuses on the uncertainty in the data itself. ...

doi:10.1002/wics.1575 pmid:36583207 pmcid:PMC9787023 fatcat:celfuif5qbacvdsl545uan2tde

In the non-linear case, the method is extended by introducing a resampling technique and by clustering the obtained results for stability of the estimate. ... In the linear case, the application of derivative-based saliency yields a commonly adopted ranking criterion. ... To address all these issues, we propose a method we term "Random Voronoi Ensemble" since it is based on random Voronoi partitions as described above; these partitions are replicated by resampling, so the ...

doi:10.1007/978-3-540-45216-4_34 fatcat:nbrmz2v7wbf6bey4qeoapi4zmq

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