Ensemble clustering based on evidence extracted from the co-association matrix

Zhong, Chongquan; Hu, Lianyu; Yue, Xiaodong; Luo, Ting; Fu, Qiang; Xu, Haiyong

doi:10.1016/j.patcog.2019.03.020

Cited by 29 publications

(5 citation statements)

References 44 publications

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“…For segmenting patches, a novel ensemble segmentation method is proposed in this study. The ensemble models consist of several single models and combine their results to make the final result 20,33 Previous studies have shown that using ensemble models can outperform the single models in classification 21 and/or clustering applications 22 . A few previous studies have been used ensemble models for medical image segmentation applications 40 …”

Section: Methodsmentioning

confidence: 99%

Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images

Khatibi

Rezaei

Fashtami

et al. 2020

Skin Research and Technology

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Background Vitiligo is an acquired pigmentary skin disorder characterized by depigmented macules and patches which brings many challenges for the patients suffering from. For vitiligo severity assessment, several scoring methods have been proposed based on morphometry and colorimetry. But, all methods suffer from much inter‐ and intra‐observer variations for estimating the depigmented area. For all mentioned assessment methods of vitiligo disorder, accurate segmentation of the skin images for lesion detection and localization is required. The image segmentation for localizing vitiligo skin lesions has many challenges because of illumination variation, different shapes and sizes of vitiligo lesions, vague lesion boundaries and skin hairs and vignette effects. The manual image segmentation is a tedious and time‐consuming task. Therefore, using automatic image segmentation methods for lesion detection is necessarily required. Materials and methods In this study, a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) methods is proposed for localizing vitiligo lesions in skin images. Unsupervised segmentation methods do not require prior manual segmentation of vitiligo lesions which is a tedious and time‐consuming task with intra‐ and inter‐observer variations. Results Our collected dataset includes 877 images taken from 21 patients with the resolution of 5760*3840 pixels suffering from vitiligo disorder. Experimental results show that SEDCIS outperforms the compared methods with accuracy of 97%, sensitivity of 98%, specificity of 96%, area overlapping of 94%, and Dice index of 97%. Conclusion The proposed method can segment vitiligo lesions with highly reasonable performance and can be used for assessing the vitiligo lesion surface.

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Section: Methodsmentioning

confidence: 99%

Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images

Khatibi

Rezaei

Fashtami

et al. 2020

Skin Research and Technology

View full text Add to dashboard Cite

show abstract

“…Cohesion and stability [27], which concentrate on the density-based connectivity between C i and other clusters in π and the inner density-based connectivity of π, are served as the third and last objectives, which are respectively defined as follows:…”

Section: B Objective Functionsmentioning

confidence: 99%

Learning to Optimize: Reference Vector Reinforcement Learning Adaption to Constrained Many-Objective Optimization of Industrial Copper Burdening System

Guo

et al. 2022

IEEE Trans. Cybern.

106

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Patient stratification has been studied widely to tackle subtype diagnosis problems for effective treatment. Due to the dimensionality curse and poor interpretability of data, there is always a long-lasting challenge in constructing a stratification model with high diagnostic ability and good generalization. To address these problems, this paper proposes two novel evolutionary multiobjective clustering algorithms with ensemble (NSGA-II-ECFE and MOEA/D-ECFE) with four cluster validity indices used as the objective functions. First, an effective ensemble construction method is developed to enrich the ensemble diversity. After that, an ensemble clustering fitness evaluation (ECFE) method is proposed to evaluate the ensembles by measuring the consensus clustering under those four objective functions. To generate the consensus clustering, ECFE exploits the hybrid co-association matrix from the ensembles and then dynamically selects the suitable clustering algorithm on that matrix. Multiple experiments have been conducted to demonstrate the effectiveness of the proposed algorithm in comparison with seven clustering algorithms, twelve ensemble clustering approaches, and two multiobjective clustering algorithms on 55 synthetic datasets and 35 real patient stratification datasets. The experimental results demonstrate the competitive edges of the proposed algorithms over those compared methods. Furthermore, the proposed algorithm is applied to extend its advantages by identifying cancer subtypes from five cancer-related single-cell RNA-seq datasets.

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“…NSCR [26] adopts the nonnegative spectral analysis to select the most discriminative features information. And for learning the best representations information, a novel robust structured NMF learning framework is proposed in [27]. DEC [28] is to collaboratively explore the rich context information of social images.…”

Section: Related Workmentioning

confidence: 99%

A new Ensemble Clustering Algorithm using a Reconstructed Mapping Coefficient

2020

KSII TIIS

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Ensemble clustering commonly integrates multiple basic partitions to obtain a more accurate clustering result than a single partition. Specifically, it exists an inevitable problem that the incomplete transformation from the original space to the integrated space. In this paper, a novel ensemble clustering algorithm using a newly reconstructed mapping coefficient (ECRMC) is proposed. In the algorithm, a newly reconstructed mapping coefficient between objects and micro-clusters is designed based on the principle of increasing information entropy to enhance effective information. This can reduce the information loss in the transformation from micro-clusters to the original space. Then the correlation of the micro-clusters is creatively calculated by the Spearman coefficient. Therefore, the revised co-association graph between objects can be built more accurately because the supplementary information can well ensure the completeness of the whole conversion process. Experiment results demonstrate that the ECRMC clustering algorithm has high performance, effectiveness, and feasibility.

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Ensemble clustering based on evidence extracted from the co-association matrix

Cited by 29 publications

References 44 publications

Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images

Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images

Learning to Optimize: Reference Vector Reinforcement Learning Adaption to Constrained Many-Objective Optimization of Industrial Copper Burdening System

A new Ensemble Clustering Algorithm using a Reconstructed Mapping Coefficient

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