Evolutionary Multiobjective Clustering and Its Applications to Patient Stratification.
Evolutionary Multiobjective Clustering and Its Applications to Patient Stratification.
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DOI：
10.1109/tcyb.2018.2817480
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发表时间：
2019-05-01
影响因子：
11.8
通讯作者：
Wong, Ka-Chun
中科院分区：
文献类型：
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作者：
Li, Xiangtao;Wong, Ka-Chun
患者分层在实现高效和个性化医疗方面发挥着重要作用。患者分层的一个重要任务是发现疾病亚型以进行有效治疗。为了实现这一目标，在过去的几十年里，聚类算法的研究引起了学术界和医学界的关注。然而，现有的聚类算法受到现实的限制，如实验噪声，高维数，并解释性差。特别是，现有的聚类算法通常只使用一个内部评价函数来确定聚类质量。不幸的是，很明显，一个内部评价函数很难对所有数据集进行拟合和鲁棒性。因此，本文提出了一种新的多目标框架，称为多目标聚类算法，通过快速搜索和发现密度峰值，以解决这些限制。在该框架中，一个参数候选人口下的多个目标，自动选择功能和评估聚类密度。为了指导多目标演化，选取了紧凑度、分离度、Calinski-Harabasz指数、Davies-Bouldin指数和Dunn指数等5个聚类有效性指标作为目标函数，捕捉了演化聚类的多个特征.采用基于分解的多目标差分进化算法对这五个目标函数同时进行优化。为了证明其有效性，已经进行了广泛的实验，将所提出的算法与45种算法进行比较，包括9种最先进的聚类算法，5种多目标进化算法，以及31种不同目标子集下的基线算法，94个数据集包括35个真实的患者分层数据集，55个基于真实的人类转录调控网络模型的合成数据集，和其他四个医学数据集数值结果表明，该算法可以获得更好的或有竞争力的解决方案比其他人。此外，时间复杂度分析，收敛性分析和参数分析，从不同的角度证明了所提出的算法的鲁棒性。
Patient stratification has a major role in enabling efficient and personalized medicine. An important task in patient stratification is to discover disease subtypes for effective treatment. To achieve this goal, the research on clustering algorithms for patient stratification has brought attention from both academia and medical community over the past decades. However, existing clustering algorithms suffer from realistic restrictions such as experimental noises, high dimensionality, and poor interpretability. In particular, the existing clustering algorithms usually determine clustering quality using only one internal evaluation function. Unfortunately, it is obvious that one internal evaluation function is hard to be fitted and robust for all datasets. Therefore, in this paper, a novel multiobjective framework called multiobjective clustering algorithm by fast search and find of density peaks is proposed to address those limitations altogether. In the proposed framework, a parameter candidate population is evolved under multiple objectives to select features and evaluate clustering densities automatically. To guide the multiobjective evolution, five cluster validity indices including compactness, separation, Calinski-Harabasz index, Davies-Bouldin index, and Dunn index, are chosen as the objective functions, capturing multiple characteristics of the evolving clusters. Multiobjective differential evolution algorithm based on decomposition is adopted to optimize those five objective functions simultaneously. To demonstrate its effectiveness, extensive experiments have been conducted, comparing the proposed algorithm with 45 algorithms including nine state-of-the-art clustering algorithms, five multiobjective evolutionary algorithms, and 31 baseline algorithms under different objective subsets on 94 datasets featuring 35 real patient stratification datasets, 55 synthetic datasets based on a real human transcription regulation network model, and four other medical datasets. The numerical results reveal that the proposed algorithm can achieve better or competitive solutions than the others. Besides, time complexity analysis, convergence analysis, and parameter analysis are conducted to demonstrate the robustness of the proposed algorithm from different perspectives.