Evolutionary Multiobjective Clustering Algorithms With Ensemble for Patient Stratification

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

12 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)11027-11040
Journal / PublicationIEEE Transactions on Cybernetics
Volume52
Issue number10
Online published7 May 2021
Publication statusPublished - Oct 2022

Abstract

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 article 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.

Research Area(s)

  • Cancer, Clustering algorithms, Clustering methods, Ensemble clustering, Heuristic algorithms, Linear programming, multiobjective optimization (MOO), Optimization, patient stratification, Urban areas

Citation Format(s)

Evolutionary Multiobjective Clustering Algorithms With Ensemble for Patient Stratification. / Wang, Yunhe; Li, Xiangtao; Wong, Ka-Chun et al.
In: IEEE Transactions on Cybernetics, Vol. 52, No. 10, 10.2022, p. 11027-11040.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review