利用有机化合物的光谱数据检验无监督集成学习

IF 3 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Kedan He, Djenerly G. Massena
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引用次数: 0

摘要

对于选择合适的聚类算法的挑战,一种解决方案是将不同的聚类组合成一个一致的聚类结果,称为聚类集成(CE)。这种集成学习策略可以跨不同的领域和数据集提供更健壮和稳定的解决方案。不幸的是,并不是集合中的所有聚类都对最终的数据分区有贡献。集群集成选择(CES)旨在从大型集群解决方案库中选择一个子集,以形成一个较小的集群集成,该集群集成的性能与所有可用的集群解决方案集一样好,甚至更好。在本文中,我们研究了四种利用高维红外和拉曼光谱数据对结构不同的有机化合物进行分类的CES方法。单一质量选择(SQI)通过选择最高质量的集成成员形成集成的子集。单一质量选择(SQI)方法与各种质量指标结合使用,通过包含最高质量的集合成员来选择子集。Bagging方法通常应用于监督学习,通过计算集合成员之间的归一化互信息(NMI)和从完整集合的随机抽样子集生成的共识解来对集合成员进行排序。层次聚类选择方法(HCAS-SQI)利用集合成员的多样性矩阵来选择质量最高的集合成员。此外,可以使用组合策略将使用多质量指标(HCAS-MQI)选择的子集组合在一起,以改进集成中的聚类解。红外+拉曼混合集合库是通过合并有机化合物的两个互补“视图”而创建的。这个本质上更加多样化的库提供了最好的全集成一致结果。总的来说,Bagging方法是推荐的,因为它提供了比完整集合共识解决方案更好或可与之相比的最可靠的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Examining unsupervised ensemble learning using spectroscopy data of organic compounds

Examining unsupervised ensemble learning using spectroscopy data of organic compounds

One solution to the challenge of choosing an appropriate clustering algorithm is to combine different clusterings into a single consensus clustering result, known as cluster ensemble (CE). This ensemble learning strategy can provide more robust and stable solutions across different domains and datasets. Unfortunately, not all clusterings in the ensemble contribute to the final data partition. Cluster ensemble selection (CES) aims at selecting a subset from a large library of clustering solutions to form a smaller cluster ensemble that performs as well as or better than the set of all available clustering solutions. In this paper, we investigate four CES methods for the categorization of structurally distinct organic compounds using high-dimensional IR and Raman spectroscopy data. Single quality selection (SQI) forms a subset of the ensemble by selecting the highest quality ensemble members. The Single Quality Selection (SQI) method is used with various quality indices to select subsets by including the highest quality ensemble members. The Bagging method, usually applied in supervised learning, ranks ensemble members by calculating the normalized mutual information (NMI) between ensemble members and consensus solutions generated from a randomly sampled subset of the full ensemble. The hierarchical cluster and select method (HCAS-SQI) uses the diversity matrix of ensemble members to select a diverse set of ensemble members with the highest quality. Furthermore, a combining strategy can be used to combine subsets selected using multiple quality indices (HCAS-MQI) for the refinement of clustering solutions in the ensemble. The IR + Raman hybrid ensemble library is created by merging two complementary “views” of the organic compounds. This inherently more diverse library gives the best full ensemble consensus results. Overall, the Bagging method is recommended because it provides the most robust results that are better than or comparable to the full ensemble consensus solutions.

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来源期刊
Journal of Computer-Aided Molecular Design
Journal of Computer-Aided Molecular Design 生物-计算机:跨学科应用
CiteScore
8.00
自引率
8.60%
发文量
56
审稿时长
3 months
期刊介绍: The Journal of Computer-Aided Molecular Design provides a form for disseminating information on both the theory and the application of computer-based methods in the analysis and design of molecules. The scope of the journal encompasses papers which report new and original research and applications in the following areas: - theoretical chemistry; - computational chemistry; - computer and molecular graphics; - molecular modeling; - protein engineering; - drug design; - expert systems; - general structure-property relationships; - molecular dynamics; - chemical database development and usage.
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