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He G, Jiang W, Peng R, Yin M, Han M. Soft Subspace Based Ensemble Clustering for Multivariate Time Series Data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:7761-7774. [PMID: 35157594 DOI: 10.1109/tnnls.2022.3146136] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Recently, multivariate time series (MTS) clustering has gained lots of attention. However, state-of-the-art algorithms suffer from two major issues. First, few existing studies consider correlations and redundancies between variables of MTS data. Second, since different clusters usually exist in different intrinsic variables, how to efficiently enhance the performance by mining the intrinsic variables of a cluster is challenging work. To deal with these issues, we first propose a variable-weighted K-medoids clustering algorithm (VWKM) based on the importance of a variable for a cluster. In VWKM, the proposed variable weighting scheme could identify the important variables for a cluster, which can also provide knowledge and experience to related experts. Then, a Reverse nearest neighborhood-based density Peaks approach (RP) is proposed to handle the problem of initialization sensitivity of VWKM. Next, based on VWKM and the density peaks approach, an ensemble Clustering framework (SSEC) is advanced to further enhance the clustering performance. Experimental results on ten MTS datasets show that our method works well on MTS datasets and outperforms the state-of-the-art clustering ensemble approaches.
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Li Y, Xu K, Ding Y, Sun Z, Ke T. A parallel CF tree clustering algorithm for mixed-type datasets. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-224234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Many traditional clustering algorithms are incapable of processing mixed-type datasets in parallel, limiting their applications in big data. In this paper, we propose a CF tree clustering algorithm based on MapReduce to handle mixed-type datasets. Mapper phase and reducer phase are the two primary phases of MR-CF. In the mapper phase, the original CF tree algorithm is modified to collect intermediate CF entries, and in the reducer phase, k-prototypes is extended to cluster CF entries. To avoid the high costs associated with I/O overheads and data serialization, MR-CF loads a dataset from HDFS only once. We first analyze the time complexity, space complexity, and I/O complexity of MR-CF. We also compare it with sklearn BIRCH, Apache Mahout k-means, k-prototypes, and mrk-prototypes on several real-world datasets and synthetic datasets. Experiments on two mixed-type big datasets reveal that MR-CF reduces execution time by 45.4% and 61.3% when compared to k-prototypes, and it reduces execution time by 73.8% and 55.0% when compared to mrk-prototypes.
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Affiliation(s)
- Yufeng Li
- College of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin, China
| | - Keyi Xu
- College of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin, China
| | - Yumei Ding
- College of Sciences, Tianjin University of Science & Technology, Tianjin, China
| | - Zhiwei Sun
- College of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin, China
| | - Ting Ke
- College of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin, China
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3
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Semi-supervised and un-supervised clustering: A review and experimental evaluation. INFORM SYST 2023. [DOI: 10.1016/j.is.2023.102178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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4
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Vahmiyan M, Kheirabadi M, Akbari E. Feature selection methods in microarray gene expression data: a systematic mapping study. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07661-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/07/2022]
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Wang G, Zhao M, Li J, Li G, Zheng F, Xu G, Hong X. m7G-Associated subtypes, tumor microenvironment, and validation of prognostic signature in lung adenocarcinoma. Front Genet 2022; 13:954840. [PMID: 36046251 PMCID: PMC9422053 DOI: 10.3389/fgene.2022.954840] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 06/27/2022] [Indexed: 12/14/2022] Open
Abstract
Background: 7-Methylguanosine (m7G) is an important posttranscriptional modification that regulates gene expression and is involved in tumorigenesis and development. Tumor microenvironment has been proven to be highly involved in tumor progression and prognosis. However, how m7G-associated genes affect the tumor microenvironment of patients with lung adenocarcinoma (LUAD) remains to be further clarified. Methods: The genetic alterations of m7G-associated genes and their associations with the prognosis and tumor microenvironment in LUAD patients were systemically analyzed. An m7G-Riskscore was established and analyzed for its performance in disease prognosis and association with patient response to immunotherapy. Expression of the model genes at the protein level was investigated through ex vivo experiments. A nomogram was finally obtained based on the m7G-Riskscore and several significant clinical pathological features. Results: m7G-Associated genes were obtained from five LUAD datasets from The Cancer Genome Atlas and Gene Expression Omnibus databases, and their expression pattern was determined. Based on the m7G-associated genes, three LUAD clusters were defined. The differentially expressed genes from the three clusters were screened and used to further divide the LUAD patients into two gene clusters. It was demonstrated that the alterations of m7G-associated genes were associated with the clinical pathological features, prognosis, and tumor immune infiltration in LUAD patients. An m7G-Riskscore including CAND1, RRM2, and SLC2A1 was obtained with robust and accurate prognostic performance. WB and cell immunofluorescence also showed significant dysregulation of CAND1, RRM2, and SLC2A1 in LUAD. In addition, a nomogram was established to improve the clinical feasibility of the m7G-Riskscore. Correlation analysis revealed that patients with a lower m7G-Riskscore had higher immune and stromal scores, responded well to chemotherapeutics and multiple targeted drugs, and survived longer. Patients with a higher m7G-Riskscore tended to suffer from a higher tumor mutation burden. Furthermore, the m7G-Riskscore exhibited significant associations with immune cell infiltration and cancer stemness. Conclusion: This study systemically analyzed m7G-associated genes and identified their potential role in tumor microenvironment and prognosis in patients with LUAD. The findings of the present study may help better understand LUAD from the m7G perspective and also provide a new thought toward the prognosis and treatment of LUAD.
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Affiliation(s)
- Guangyao Wang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, China
| | - Mei Zhao
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, China
| | - Jiao Li
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, China
| | - Guosheng Li
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, China
| | - Fukui Zheng
- Department of Intensive Care Unit, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, China
| | - Guanglan Xu
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, China
- *Correspondence: Guanglan Xu, ; Xiaohua Hong,
| | - Xiaohua Hong
- Graduate School, Guangxi University of Chinese Medicine, Nanning, China
- *Correspondence: Guanglan Xu, ; Xiaohua Hong,
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Liu J, Wang Y, Meng H, Yin Y, Zhu H, Ni T. Identification of the Prognostic Signature Associated With Tumor Immune Microenvironment of Uterine Corpus Endometrial Carcinoma Based on Ferroptosis-Related Genes. Front Cell Dev Biol 2021; 9:735013. [PMID: 34692692 PMCID: PMC8526722 DOI: 10.3389/fcell.2021.735013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 09/13/2021] [Indexed: 01/31/2023] Open
Abstract
Background: Uterine corpus endometrial carcinoma (UCEC) is the sixth most common cancer worldwide. Ferroptosis plays an important role in malignant tumors. However, the study of ferroptosis in the endometrial carcinoma remains blank. Methods: First, we constructed a ferroptosis-related signature based on the expression profiles from The Cancer Genome Atlas database. Then, patients were divided into the high-risk and low-risk groups based on this signature. The signature was evaluated by Kaplan–Meier analysis and receiver operating characteristic (ROC) analysis. We further investigated the relationship between this signature and immune microenvironment via CIBERSORT algorithm, ImmuCellAI, MAF, MSI sensor algorithm, GSEA, and GDSC. Results: This signature could be an independent prognostic factor based on multivariate Cox regression analysis. GSEA revealed that this signature was associated with immune-related phenotype. In addition, we indicated the different status of immune infiltration and response to the immune checkpoint between low-risk and high-risk groups. Patients in the low-risk group were more likely to present with a higher expression of immune checkpoint molecules and tumor mutation burden. Meanwhile, the low-risk patients showed sensitive responses to chemotherapy drugs. Conclusion: In summary, the six ferroptosis-related genes signature could be used in molecular subgrouping and accurately predict the prognosis of UCEC.
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Affiliation(s)
- Jinhui Liu
- Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yichun Wang
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Huangyang Meng
- Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yin Yin
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hongjun Zhu
- Department of Oncology, Nantong Third People's Hospital Affiliated to Nantong University, Nantong, China
| | - Tingting Ni
- Department of Oncology, Affiliated Tumor Hospital to Nantong University, Nantong, China
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Jiang X, Chen M, Song W, Lin GN. Label propagation-based semi-supervised feature selection on decoding clinical phenotypes with RNA-seq data. BMC Med Genomics 2021; 14:141. [PMID: 34465339 PMCID: PMC8406783 DOI: 10.1186/s12920-021-00985-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 05/14/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Clinically, behavior, cognitive, and mental functions are affected during the neurodegenerative disease progression. To date, the molecular pathogenesis of these complex disease is still unclear. With the rapid development of sequencing technologies, it is possible to delicately decode the molecular mechanisms corresponding to different clinical phenotypes at the genome-wide transcriptomic level using computational methods. Our previous studies have shown that it is difficult to distinguish disease genes from non-disease genes. Therefore, to precisely explore the molecular pathogenesis under complex clinical phenotypes, it is better to identify biomarkers corresponding to different disease stages or clinical phenotypes. So, in this study, we designed a label propagation-based semi-supervised feature selection approach (LPFS) to prioritize disease-associated genes corresponding to different disease stages or clinical phenotypes. METHODS In this study, we pioneering put label propagation clustering and feature selection into one framework and proposed label propagation-based semi-supervised feature selection approach. LPFS prioritizes disease genes related to different disease stages or phenotypes through the alternative iteration of label propagation clustering based on sample network and feature selection with gene expression profiles. Then the GO and KEGG pathway enrichment analysis were carried as well as the gene functional analysis to explore molecular mechanisms of specific disease phenotypes, thus to decode the changes in individual behavioral and mental characteristics during neurodegenerative disease progression. RESULTS Large amounts of experiments were conducted to verify the performance of LPFS with Huntington's gene expression data. Experimental results shown that LPFS performs better in comparison with the-state-of-art methods. GO and KEGG enrichment analysis of key gene sets shown that TGF-beta signaling pathway, cytokine-cytokine receptor interaction, immune response, and inflammatory response were gradually affected during the Huntington's disease progression. In addition, we found that the expression of SLC4A11, ZFP474, AMBP, TOP2A, PBK, CCDC33, APSL, DLGAP5, and Al662270 changed seriously by the development of the disease. CONCLUSIONS In this study, we designed a label propagation-based semi-supervised feature selection model to precisely selected key genes of different disease phenotypes. We conducted experiments using the model with Huntington's disease mice gene expression data to decode the mechanisms of it. We found many cell types, including astrocyte, microglia, and GABAergic neuron, could be involved in the pathological process.
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Affiliation(s)
- Xue Jiang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030 China
| | - Miao Chen
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030 China
| | - Weichen Song
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030 China
| | - Guan Ning Lin
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030 China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 200030 China
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8
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Liu B, Liu Y, Zhang H, Xu Y, Tang C, Tang L, Qin H, Miao C. Adaptive Power Iteration Clustering. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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9
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Sarkar A, Murugan TS. Analysis on dual algorithms for optimal cluster head selection in wireless sensor network. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-020-00546-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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10
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Li J, Xie L, Xie Y, Wang F. Bregmannian consensus clustering for cancer subtypes analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 189:105337. [PMID: 31962279 DOI: 10.1016/j.cmpb.2020.105337] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 01/09/2020] [Accepted: 01/09/2020] [Indexed: 06/10/2023]
Abstract
Cancer subtype analysis, as an extension of cancer diagnosis, can be regarded as a consensus clustering problem. This analysis is beneficial for providing patients with more accurate treatment. Consensus clustering refers to a situation in which several different clusters have been obtained for a particular data set, and it is desired to aggregate those clustering results to get a better clustering solution. In this paper, we propose to generalize the traditional consensus clustering methods in three manners: (1) We provide Bregmannian consensus clustering (BCC), where the loss between the consensus clustering result and all the input clusterings are generalized from a traditional Euclidean distance to a general Bregman loss; (2) we generalize the BCC to a weighted case, where each input clustering has different weights, providing a better solution for the final clustering result; and (3) we propose a novel semi-supervised consensus clustering, which adds some must-link and cannot-link constraints compared with the first two methods. Then, we obtain three cancer (breast, lung, colorectal cancer) data sets from The Cancer Genome Atlas (TCGA). Each data set has three data types (mRNA, mircoRNA, methylation), and each is respectively used to test the accuracy of the proposed algorithms for clusterings. The experimental results demonstrate that the highest aggregation accuracy of the weighted BCC (WBCC) on cancer data sets is 90.2%. Moreover, although the lowest accuracy is 62.3%, it is higher than other methods on the same data set. Therefore, we conclude that as compared with the competition, our method is more effective.
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Affiliation(s)
- Jianqiang Li
- School of Software, Beijing University of Technology, China.
| | - Liyang Xie
- School of Software, Beijing University of Technology, China
| | - Yunshen Xie
- School of Software, Beijing University of Technology, China
| | - Fei Wang
- Weill Cornell Medical College, Cornell University, USA
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Ye X, Zhao J, Zhang L, Guo L. A Nonparametric Deep Generative Model for Multimanifold Clustering. IEEE TRANSACTIONS ON CYBERNETICS 2019; 49:2664-2677. [PMID: 29993595 DOI: 10.1109/tcyb.2018.2832171] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Multimanifold clustering separates data points approximately lying on a union of submanifolds into several clusters. In this paper, we propose a new nonparametric Bayesian model to handle the manifold data structure. In our framework, we first model the manifold mapping function between Euclidean space and topological space by applying a deep neural network, and then construct the corresponding generation process of multiple manifold data. To solve the posterior approximation problem, in the optimization procedure, we apply a variational auto-encoder-based optimization algorithm. Especially, as the manifold algorithm has poor performance on the real dataset where nonmanifold and manifold clusters are appearing simultaneously, we expand our proposed manifold algorithm by integrating it with the original Dirichlet process mixture model. Experimental results have been carried out to demonstrate the state-of-the-art clustering performance.
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12
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A survey of neural network-based cancer prediction models from microarray data. Artif Intell Med 2019; 97:204-214. [PMID: 30797633 DOI: 10.1016/j.artmed.2019.01.006] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 10/22/2018] [Accepted: 01/27/2019] [Indexed: 12/17/2022]
Abstract
Neural networks are powerful tools used widely for building cancer prediction models from microarray data. We review the most recently proposed models to highlight the roles of neural networks in predicting cancer from gene expression data. We identified articles published between 2013-2018 in scientific databases using keywords such as cancer classification, cancer analysis, cancer prediction, cancer clustering and microarray data. Analyzing the studies reveals that neural network methods have been either used for filtering (data engineering) the gene expressions in a prior step to prediction; predicting the existence of cancer, cancer type or the survivability risk; or for clustering unlabeled samples. This paper also discusses some practical issues that can be considered when building a neural network-based cancer prediction model. Results indicate that the functionality of the neural network determines its general architecture. However, the decision on the number of hidden layers, neurons, hypermeters and learning algorithm is made using trail-and-error techniques.
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Jiao H, Zhang L, Shen Q, Zhu J, Shi P. Robust Gene Circuit Control Design for Time-Delayed Genetic Regulatory Networks Without SUM Regulatory Logic. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:2086-2093. [PMID: 29993838 DOI: 10.1109/tcbb.2018.2825445] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This paper investigates the gene circuit control design problem of time-delayed genetic regulatory networks. In the genetic regulatory networks, the time delays are unknown constants, and the genetic regulatory is not conventional SUM regulatory logic and can be modeled to be an unknown nonlinear function of the time-delayed states of the other genes in a cell. By Lyapunov stability, a novel adaptive gene circuit control design approach is proposed for the genetic regulatory networks, where the unknown time delays are estimated online by adaptive algorithms and the unknown regulatory functions are approximated by neural networks. The design approach in this paper is delay-dependent and has less conservatism than the delay-independent approach. From theoretical analysis, the closed-loop system is asymptotically stable and all the signals in the system converge to an adjustable neighborhood of the origin. Finally, a numerical example is given to show the effectiveness of the new design approach.
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Wang Z, Yu Z, Chen CLP, You J, Gu T, Wong HS, Zhang J. Clustering by Local Gravitation. IEEE TRANSACTIONS ON CYBERNETICS 2018; 48:1383-1396. [PMID: 28475072 DOI: 10.1109/tcyb.2017.2695218] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The objective of cluster analysis is to partition a set of data points into several groups based on a suitable distance measure. We first propose a model called local gravitation among data points. In this model, each data point is viewed as an object with mass, and associated with a local resultant force (LRF) generated by its neighbors. The motivation of this paper is that there exist distinct differences between the LRFs (including magnitudes and directions) of the data points close to the cluster centers and at the boundary of the clusters. To capture these differences efficiently, two new local measures named centrality and coordination are further investigated. Based on empirical observations, two new clustering methods called local gravitation clustering and communication with local agents are designed, and several test cases are conducted to verify their effectiveness. The experiments on synthetic data sets and real-world data sets indicate that both clustering approaches achieve good performance on most of the data sets.
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Yu Z, Lu Y, Zhang J, You J, Wong HS, Wang Y, Han G. Progressive Semisupervised Learning of Multiple Classifiers. IEEE TRANSACTIONS ON CYBERNETICS 2018; 48:689-702. [PMID: 28113355 DOI: 10.1109/tcyb.2017.2651114] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Semisupervised learning methods are often adopted to handle datasets with very small number of labeled samples. However, conventional semisupervised ensemble learning approaches have two limitations: 1) most of them cannot obtain satisfactory results on high dimensional datasets with limited labels and 2) they usually do not consider how to use an optimization process to enlarge the training set. In this paper, we propose the progressive semisupervised ensemble learning approach (PSEMISEL) to address the above limitations and handle datasets with very small number of labeled samples. When compared with traditional semisupervised ensemble learning approaches, PSEMISEL is characterized by two properties: 1) it adopts the random subspace technique to investigate the structure of the dataset in the subspaces and 2) a progressive training set generation process and a self evolutionary sample selection process are proposed to enlarge the training set. We also use a set of nonparametric tests to compare different semisupervised ensemble learning methods over multiple datasets. The experimental results on 18 real-world datasets from the University of California, Irvine machine learning repository show that PSEMISEL works well on most of the real-world datasets, and outperforms other state-of-the-art approaches on 10 out of 18 datasets.
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Yu Z, Wang Z, You J, Zhang J, Liu J, Wong HS, Han G. A New Kind of Nonparametric Test for Statistical Comparison of Multiple Classifiers Over Multiple Datasets. IEEE TRANSACTIONS ON CYBERNETICS 2017; 47:4418-4431. [PMID: 28113414 DOI: 10.1109/tcyb.2016.2611020] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Nonparametric statistical analysis, such as the Friedman test (FT), is gaining more and more attention due to its useful applications in a lot of experimental studies. However, traditional FT for the comparison of multiple learning algorithms on different datasets adopts the naive ranking approach. The ranking is based on the average accuracy values obtained by the set of learning algorithms on the datasets, which neither considers the differences of the results obtained by the learning algorithms on each dataset nor takes into account the performance of the learning algorithms in each run. In this paper, we will first propose three kinds of ranking approaches, which are the weighted ranking approach, the global ranking approach (GRA), and the weighted GRA. Then, a theoretical analysis is performed to explore the properties of the proposed ranking approaches. Next, a set of the modified FTs based on the proposed ranking approaches are designed for the comparison of the learning algorithms. Finally, the modified FTs are evaluated through six classifier ensemble approaches on 34 real-world datasets. The experiments show the effectiveness of the modified FTs.
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Liu W, Ye M, Wei J, Hu X. Compressed constrained spectral clustering framework for large-scale data sets. Knowl Based Syst 2017. [DOI: 10.1016/j.knosys.2017.08.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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18
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Fast Constrained Spectral Clustering and Cluster Ensemble with Random Projection. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017; 2017:2658707. [PMID: 29312447 PMCID: PMC5632995 DOI: 10.1155/2017/2658707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Accepted: 08/01/2017] [Indexed: 11/18/2022]
Abstract
Constrained spectral clustering (CSC) method can greatly improve the clustering accuracy with the incorporation of constraint information into spectral clustering and thus has been paid academic attention widely. In this paper, we propose a fast CSC algorithm via encoding landmark-based graph construction into a new CSC model and applying random sampling to decrease the data size after spectral embedding. Compared with the original model, the new algorithm has the similar results with the increase of its model size asymptotically; compared with the most efficient CSC algorithm known, the new algorithm runs faster and has a wider range of suitable data sets. Meanwhile, a scalable semisupervised cluster ensemble algorithm is also proposed via the combination of our fast CSC algorithm and dimensionality reduction with random projection in the process of spectral ensemble clustering. We demonstrate by presenting theoretical analysis and empirical results that the new cluster ensemble algorithm has advantages in terms of efficiency and effectiveness. Furthermore, the approximate preservation of random projection in clustering accuracy proved in the stage of consensus clustering is also suitable for the weighted k-means clustering and thus gives the theoretical guarantee to this special kind of k-means clustering where each point has its corresponding weight.
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20
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Lu H, Yang L, Yan K, Xue Y, Gao Z. A cost-sensitive rotation forest algorithm for gene expression data classification. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.09.077] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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21
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Yu X, Yu G, Wang J. Clustering cancer gene expression data by projective clustering ensemble. PLoS One 2017; 12:e0171429. [PMID: 28234920 PMCID: PMC5325197 DOI: 10.1371/journal.pone.0171429] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 01/20/2017] [Indexed: 11/19/2022] Open
Abstract
Gene expression data analysis has paramount implications for gene treatments, cancer diagnosis and other domains. Clustering is an important and promising tool to analyze gene expression data. Gene expression data is often characterized by a large amount of genes but with limited samples, thus various projective clustering techniques and ensemble techniques have been suggested to combat with these challenges. However, it is rather challenging to synergy these two kinds of techniques together to avoid the curse of dimensionality problem and to boost the performance of gene expression data clustering. In this paper, we employ a projective clustering ensemble (PCE) to integrate the advantages of projective clustering and ensemble clustering, and to avoid the dilemma of combining multiple projective clusterings. Our experimental results on publicly available cancer gene expression data show PCE can improve the quality of clustering gene expression data by at least 4.5% (on average) than other related techniques, including dimensionality reduction based single clustering and ensemble approaches. The empirical study demonstrates that, to further boost the performance of clustering cancer gene expression data, it is necessary and promising to synergy projective clustering with ensemble clustering. PCE can serve as an effective alternative technique for clustering gene expression data.
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Affiliation(s)
- Xianxue Yu
- College of Computer and Information Science, Southwest University, Beibei, Chongqing, China
| | - Guoxian Yu
- College of Computer and Information Science, Southwest University, Beibei, Chongqing, China
| | - Jun Wang
- College of Computer and Information Science, Southwest University, Beibei, Chongqing, China
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22
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Du M, Ding S, Xue Y. A robust density peaks clustering algorithm using fuzzy neighborhood. INT J MACH LEARN CYB 2017. [DOI: 10.1007/s13042-017-0636-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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