A new classifier ensemble methodology based on subspace learning

Parvïn, Hamïd; Alinejad‐Rokny, Hamid; Minaei-Bidgoli, Behrouz; Parvin, Sajad

doi:10.1080/0952813x.2012.715683

Cited by 44 publications

(8 citation statements)

References 30 publications

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“…Our literature searches were focused on human and mice English language papers available in the PubMed, Scopus, and Web of Science. We also used data and text mining techniques to extract additional related studies [58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73]. A knowledgebased filtering system technique has been also used to categoriz e the texts from the literatures search [74][75][76][77][78][79].…”

Section: Literature Search Strategymentioning

confidence: 99%

An Integrative Analysis of Chromatin Interactions, Gene Expression and Genomic Variants Identifies 30 Likely Regulatory Variants in Prostate Cancer

Labani¹,

Beheshti²,

Argha³

et al. 2023

Preprint

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Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies showed that genomic alterations represent the most common mechanism for molecular alterations that cause the development and progression of PC. Great efforts have been done to identify common protein-coding genetic variations; however, the impact of non-coding variations including regulatory genetic variants is not still well understood. To gain an understanding of the functional impact of genetic variants, particularly, regulatory variants in PC, we developed an integrative pipeline (AGV) that used whole genome/exome sequences, GWAS SNPs, chromosome conformation capture data, and ChIP-Seq signals to investigate the potential impact of genomic variants on the underlying target genes in PC. We identified 646 putative regulatory variants, of which 30 of them significantly altered the expression of at least one protein-coding gene. Our analysis of chromatin interactions data (Hi-C) revealed that the 30 putative regulatory variants may affect 131 coding and non-coding genes. Interestingly, our study showed the 131 protein-coding genes are involved in disease-related pathways including Reactome and MSigDB in which for most of them targeted treatment options are currently available. Together, our results provide a comprehensive map of genomic variants in PC and revealed their potential contribution to prostate cancer progression and development.

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Section: Literature Search Strategymentioning

confidence: 99%

An Integrative Analysis of Chromatin Interactions, Gene Expression and Genomic Variants Identifies 30 Likely Regulatory Variants in Prostate Cancer

Labani¹,

Beheshti²,

Argha³

et al. 2023

Preprint

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“…The clustering ensemble approaches with homogenous clustering algorithms employ a same clustering algorithm during generation of the ensemble pool, that is, all partitions of the ensemble pool are generated by a same clustering algorithm. The partitions of the ensemble pool in homogenous clustering algorithms can be produced by one of the following subtypes: by employing different initializations of a given clustering algorithm , by employing different parameters (like different numbers of clusters) for data clustering using a same clustering algorithm , by employing different data projections for data clustering using a same clustering algorithm , by employing different subsets of dataset features for data clustering using a same clustering algorithm , by employing meta heuristic algorithms for data clustering , and by employing different datasets for data clustering using a same clustering algorithm. …”

Section: Related Workmentioning

confidence: 99%

“…Cluster ensemble combines multiple data partitions into a single usually better partition often referred to as consensus partition. The consensus partition is usually better in terms of stability, quality, and robustness [42,57]. The quality of a partition can be evaluated by an internal measure or an external measure.…”

Section: Related Workmentioning

confidence: 99%

Section: Ensemble Generation Problemmentioning

confidence: 99%

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Dependability‐based cluster weighting in clustering ensemble

Najafi

Parvïn

Mirzaie

et al. 2020

Statistical Analysis

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After observing the ensemble success in supervised learning (such as classification), it was extended into unsupervised learning. Therefore, cluster ensemble, which merges multiple basic data partitions or clusters (called as ensemble pool) into an ordinarily better clustering solution usually named as consensus partition, emerged. Any cluster ensemble method tries to optimize a particular criterion during extracting the consensus partition out of the ensemble pool. But traditional cluster ensembles consider all the pool members with the equal importance in making the consensus partition; that is to say that each basic partition or cluster participates in the cluster ensemble algorithm equivalently. Indeed, they ignore to consider any ensemble member according to its importance. But it is obvious that some clusters with more quality deserve more emphasis and some clusters with less quality deserve less emphasis during generating consensus partition. This paper proposes (a) a metric to evaluate quality of any arbitrary cluster, (b) a mechanism to project the computed quality of a cluster into a meaningful weight value, and (c) an approach to apply the weight values of the basic clusters in the cluster ensemble process. Experimental results conducted on a number of real‐world standard datasets indicate that the proposed method outperforms the state of the art methods.

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“…In [23] authors employed a subspace and clustering methodologies to generate a subspace which has a balanced number of classes in different subspaces. Although many subspace learning strategies have been proposed in [24]- [26], very few approaches exist that utilize ensemble learning to maximize the final classification accuracy. Additionally, although the strategies discussed that have successfully utilized clustering to generate random subspace to train base classifiers, however, since datasets have randomness in them a clear distinction of how many clusters should be generated to create a diverse input space which in turn will generate an ensemble classifier that can achieve the highest classification accuracy is required.…”

Section: Introductionmentioning

confidence: 99%

A Novel Diversity Measure and Classifier Selection Approach for Generating Ensemble Classifiers

Jan

Verma

2019

IEEE Access

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Accuracy and diversity are considered to be the two deriving factors when it comes to generating an ensemble classifier. Focusing only on accuracy causes the ensemble classifier to suffer from ''diminishing returns'' and the ensemble accuracy tends to plateau; whereas focusing only on diversity causes the ensemble classifier to suffer in accuracy. Therefore, a balance must be maintained between the two for the ensemble classifier to achieve high classification accuracy. In this paper, we propose a novel diversity measure known as Misclassification Diversity (MD) and an Incremental Layered Classifier Selection (ILCS) approach to generate an ensemble classifier. The proposed approach ILCS-MD generates an ensemble classifier by incrementally selecting classifiers from the base classifier pool based on increasing accuracy and diversity. The benefits are in two folds 1) the generated ensemble classifier contains only those classifiers from the pool which can either maximize accuracy whilst maintaining or increasing the diversity, and 2) the generated ensemble classifier selects only a few classifiers from the base classifier pool thus reducing ensemble component size as well. The proposed approach is evaluated on 55 benchmark datasets taken from UCI and KEEL dataset repositories. The results are compared with five existing pairwise diversity measures, and existing state of the art ensemble classifier approaches. A significance test is also conducted to verify the significance of the results.INDEX TERMS Ensemble classifiers, neural networks, multiple classifiers learning, diversity measures.

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A new classifier ensemble methodology based on subspace learning

Cited by 44 publications

References 30 publications

An Integrative Analysis of Chromatin Interactions, Gene Expression and Genomic Variants Identifies 30 Likely Regulatory Variants in Prostate Cancer

An Integrative Analysis of Chromatin Interactions, Gene Expression and Genomic Variants Identifies 30 Likely Regulatory Variants in Prostate Cancer

Dependability‐based cluster weighting in clustering ensemble

A Novel Diversity Measure and Classifier Selection Approach for Generating Ensemble Classifiers

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