Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection
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Habibollah Haron | Andri Mirzal | Haza Nuzly Abdul Hamed | Jun Chin Ang | H. Haron | Andri Mirzal | J. Ang | H. N. A. Hamed
[1] Li-Yeh Chuang,et al. Tabu Search and Binary Particle Swarm Optimization for Feature Selection Using Microarray Data , 2009, J. Comput. Biol..
[2] Thibault Helleputte,et al. Partially supervised feature selection with regularized linear models , 2009, ICML '09.
[3] Richard Weber,et al. Simultaneous feature selection and classification using kernel-penalized support vector machines , 2011, Inf. Sci..
[4] Anirban Mukherjee,et al. Cancer Classification from Gene Expression Data by NPPC Ensemble , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[5] E. Lander,et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[6] Christian A. Rees,et al. Systematic variation in gene expression patterns in human cancer cell lines , 2000, Nature Genetics.
[7] Yudong D. He,et al. Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.
[8] S. Ramaswamy,et al. Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. , 2002, Cancer research.
[9] Christian A. Rees,et al. Molecular portraits of human breast tumours , 2000, Nature.
[10] Jagath C. Rajapakse,et al. Multiclass Gene Selection Using Pareto-Fronts , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[11] Satoru Miyano,et al. Null space based feature selection method for gene expression data , 2012, Int. J. Mach. Learn. Cybern..
[12] Shutao Li,et al. Graph embedding based feature selection , 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).
[13] Tshilidzi Marwala,et al. A Population-Based Incremental Learning approach to microarray gene expression feature selection , 2010, 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel.
[14] Yihui Liu,et al. Wavelet feature extraction for high-dimensional microarray data , 2009, Neurocomputing.
[15] Alexandre d'Aspremont,et al. Clustering and feature selection using sparse principal component analysis , 2007, ArXiv.
[16] Sambasivarao Damaraju,et al. Breast cancer prediction using genome wide single nucleotide polymorphism data , 2013, BMC Bioinformatics.
[17] Taghi M. Khoshgoftaar,et al. A review of the stability of feature selection techniques for bioinformatics data , 2012, 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI).
[18] Zili Zhang,et al. A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data , 2010, BMC Bioinformatics.
[19] Fred W. Glover,et al. Tabu Search - Part I , 1989, INFORMS J. Comput..
[20] Yang Ai-jun,et al. Bayesian variable selection for disease classification using gene expression data , 2010 .
[21] Surajit Ray,et al. Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction , 2011, BMC Bioinformatics.
[22] Guy Lapalme,et al. A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..
[23] Qi Shen,et al. Simultaneous genes and training samples selection by modified particle swarm optimization for gene expression data classification , 2009, Comput. Biol. Medicine.
[24] Verónica Bolón-Canedo,et al. A review of microarray datasets and applied feature selection methods , 2014, Inf. Sci..
[25] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[26] Huan Liu,et al. Feature Selection: An Ever Evolving Frontier in Data Mining , 2010, FSDM.
[27] Zijiang Yang,et al. PLS-Based Gene Selection and Identification of Tumor-Specific Genes , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[28] Sameem Abdul Kareem,et al. Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods , 2012, BMC Bioinformatics.
[29] Haytham Elghazel,et al. Efficient semi-supervised feature selection by an ensemble approach , 2013 .
[30] Pat Langley,et al. Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..
[31] Chen-Fu Chien,et al. Cluster analysis of genome-wide expression data for feature extraction , 2009, Expert Syst. Appl..
[32] Ali Anaissi,et al. A balanced iterative random forest for gene selection from microarray data , 2013, BMC Bioinformatics.
[33] William M. Rand,et al. Objective Criteria for the Evaluation of Clustering Methods , 1971 .
[34] Torben F. Ørntoft,et al. Identifying distinct classes of bladder carcinoma using microarrays , 2003, Nature Genetics.
[35] Hugues Bersini,et al. A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[36] E. Lander,et al. MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia , 2002, Nature Genetics.
[37] Driss Aboutajdine,et al. A two-stage gene selection scheme utilizing MRMR filter and GA wrapper , 2011, Knowledge and Information Systems.
[38] David E. Misek,et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma , 2002, Nature Medicine.
[39] E. Petricoin,et al. Use of proteomic patterns in serum to identify ovarian Cancer , 2002 .
[40] Justin Doak,et al. An evaluation of feature selection methods and their application to computer security , 1992 .
[41] P. Pudil,et al. of Techniques for Large-Scale Feature Selection , 1994 .
[42] G. Victo Sudha George,et al. Review on Feature Selection Techniques and the Impact of SVM for Cancer Classification using Gene Expression Profile , 2011, ArXiv.
[43] Michal Linial,et al. Novel Unsupervised Feature Filtering of Biological Data , 2006, ISMB.
[44] Roger E Bumgarner,et al. Comparative hybridization of an array of 21,500 ovarian cDNAs for the discovery of genes overexpressed in ovarian carcinomas. , 1999, Gene.
[45] Slobodan Vucetic,et al. Improving accuracy of microarray classification by a simple multi-task feature selection filter , 2011, Int. J. Data Min. Bioinform..
[46] Anirban Mukhopadhyay,et al. An Improved Minimum Redundancy Maximum Relevance Approach for Feature Selection in Gene Expression Data , 2013 .
[47] Debahuti Mishra,et al. Feature Selection for Cancer Classification: A Signal-to-noise Ratio Approach , 2011 .
[48] Minghao Yin,et al. Multiobjective Binary Biogeography Based Optimization for Feature Selection Using Gene Expression Data , 2013, IEEE Transactions on NanoBioscience.
[49] Chris H. Q. Ding,et al. Minimum redundancy feature selection from microarray gene expression data , 2003, Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003.
[50] Ash A. Alizadeh,et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.
[51] Edward R. Dougherty,et al. Is cross-validation better than resubstitution for ranking genes? , 2004, Bioinform..
[52] Young Bun Kim,et al. Unsupervised Gene Selection For High Dimensional Data , 2006, Sixth IEEE Symposium on BioInformatics and BioEngineering (BIBE'06).
[53] Dong Ling Tong,et al. Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection , 2010, Int. J. Mach. Learn. Cybern..
[54] Yong Wang,et al. A Novel Method of Feature Selection based on SVM , 2013, J. Comput..
[55] Jianzhong Li,et al. A stable gene selection in microarray data analysis , 2006, BMC Bioinformatics.
[56] Xiaosheng Wang,et al. A Robust Gene Selection Method for Microarray-based Cancer Classification , 2010, Cancer informatics.
[57] Huan Liu,et al. Feature Selection for Clustering: A Review , 2018, Data Clustering: Algorithms and Applications.
[58] Y. Skaik. Understanding and using sensitivity, specificity and predictive values , 2008, Indian journal of ophthalmology.
[59] Robert Tibshirani,et al. A Framework for Feature Selection in Clustering , 2010, Journal of the American Statistical Association.
[60] U. Alon,et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[61] Chris H. Q. Ding,et al. Consensus group stable feature selection , 2009, KDD.
[62] Yvan Saeys,et al. Feature Selection for Classification of Nucleic Acid Sequences , 2004 .
[63] Christos Boutsidis,et al. Unsupervised feature selection for principal components analysis , 2008, KDD.
[64] Jane You,et al. Double Selection Based Semi-Supervised Clustering Ensemble for Tumor Clustering from Gene Expression Profiles , 2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[65] D. Botstein,et al. Gene expression patterns in human liver cancers. , 2002, Molecular biology of the cell.
[66] M. Daumer,et al. Evaluating Microarray-based Classifiers: An Overview , 2008, Cancer informatics.
[67] Hamid R. Rabiee,et al. Fuzzy support vector machine: an efficient rule-based classification technique for microarrays , 2013, BMC Bioinformatics.
[68] John Crowley,et al. Global gene expression profiling of multiple myeloma, monoclonal gammopathy of undetermined significance, and normal bone marrow plasma cells. , 2002, Blood.
[69] Denis Hamad,et al. Constraint scores for semi-supervised feature selection: A comparative study , 2011, Pattern Recognit. Lett..
[70] Yonghong Peng,et al. A novel feature selection approach for biomedical data classification , 2010, J. Biomed. Informatics.
[71] David B. Skalak,et al. Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms , 1994, ICML.
[72] Yuanyuan Li,et al. Feature selection based on sensitivity analysis of fuzzy ISODATA , 2012, Neurocomputing.
[73] Dongqing Xie,et al. A New Unsupervised Feature Ranking Method for Gene Expression Data Based on Consensus Affinity , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[74] Huan Liu,et al. Efficient Feature Selection via Analysis of Relevance and Redundancy , 2004, J. Mach. Learn. Res..
[75] J. Foekens,et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer , 2005, The Lancet.
[76] S. Niijima,et al. Laplacian Linear Discriminant Analysis Approach to Unsupervised Feature Selection , 2009, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[77] Thibault Helleputte,et al. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods , 2010, Bioinform..
[78] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[79] Khalid Benabdeslem,et al. Local-to-global semi-supervised feature selection , 2013, CIKM.
[80] Salwani Abdullah,et al. Hybridizing relieff, mRMR filters and GA wrapper approaches for gene selection , 2012 .
[81] Yogesh R. Shepal. A Fast Clustering-Based Feature Subset Selection Algorithm for High Dimensional Data , 2014 .
[82] D. Botstein,et al. Diversity of gene expression in adenocarcinoma of the lung , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[83] J.C. Rajapakse,et al. SVM-RFE With MRMR Filter for Gene Selection , 2010, IEEE Transactions on NanoBioscience.
[84] Wei Liang,et al. Gene Selection Using Locality Sensitive Laplacian Score , 2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[85] Gavin Brown,et al. Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection , 2012, J. Mach. Learn. Res..
[86] Kezhi Mao,et al. Recursive Mahalanobis Separability Measure for Gene Subset Selection , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[87] Tom Fawcett,et al. ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .
[88] Enrique Alba,et al. Sensitivity and specificity based multiobjective approach for feature selection: Application to cancer diagnosis , 2009, Inf. Process. Lett..
[89] Feiping Nie,et al. Multi-Class L2,1-Norm Support Vector Machine , 2011, 2011 IEEE 11th International Conference on Data Mining.
[90] Philip S. Yu,et al. Forward Semi-supervised Feature Selection , 2008, PAKDD.
[91] T. H. Bø,et al. New feature subset selection procedures for classification of expression profiles , 2002, Genome Biology.
[92] Kwong-Sak Leung,et al. Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification , 2013, BMC Bioinformatics.
[93] Bing Liu,et al. An efficient semi-unsupervised gene selection method via spectral biclustering , 2006, IEEE Transactions on NanoBioscience.
[94] Todd,et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning , 2002, Nature Medicine.
[95] Tiejun Tong,et al. Gene Selection Using Iterative Feature Elimination Random Forests for Survival Outcomes , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[96] Fred Glover,et al. Tabu Search - Part II , 1989, INFORMS J. Comput..
[97] T. Golub,et al. Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. , 2003, Cancer research.
[98] Jidong Zhao,et al. Locality sensitive semi-supervised feature selection , 2008, Neurocomputing.
[99] R. Spang,et al. Predicting the clinical status of human breast cancer by using gene expression profiles , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[100] Frédérique Bitton,et al. CATdb: a public access to Arabidopsis transcriptome data from the URGV-CATMA platform , 2007, Nucleic Acids Res..
[101] E. Dougherty,et al. Gene-expression profiles in hereditary breast cancer. , 2001, The New England journal of medicine.
[102] Gilles Brassard,et al. Fundamentals of Algorithmics , 1995 .
[103] Dong-Sheng Cao,et al. Recipe for uncovering predictive genes using support vector machines based on model population analysis , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[104] Chee Peng Lim,et al. A Modified Two-Stage SVM-RFE Model for Cancer Classification Using Microarray Data , 2011, ICONIP.
[105] Amir Jazaeri,et al. Microarray analysis reveals distinct gene expression profiles among different histologic types of endometrial cancer. , 2003, Cancer research.
[106] Huan Liu,et al. Feature selection for classification: A review , 2014 .
[107] Huan Liu,et al. Feature Selection for Classification , 1997, Intell. Data Anal..
[108] J. Downing,et al. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. , 2002, Cancer cell.
[109] S. P. Fodor. DNA SEQUENCING: Massively Parallel Genomics , 1997, Science.
[110] G. Celeux,et al. Variable Selection for Clustering with Gaussian Mixture Models , 2009, Biometrics.
[111] T. Poggio,et al. Prediction of central nervous system embryonal tumour outcome based on gene expression , 2002, Nature.
[112] Haytham Elghazel,et al. Semi-supervised Feature Importance Evaluation with Ensemble Learning , 2011, 2011 IEEE 11th International Conference on Data Mining.
[113] J. Welsh,et al. Molecular classification of human carcinomas by use of gene expression signatures. , 2001, Cancer research.
[114] Kazuyuki Murase,et al. A new wrapper feature selection approach using neural network , 2010, Neurocomputing.
[115] Yungho Leu,et al. A novel hybrid feature selection method for microarray data analysis , 2011, Appl. Soft Comput..
[116] Giorgio Valentini,et al. A Mathematical Model for the Validation of Gene Selection Methods , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[117] Chris H. Q. Ding,et al. Evolving Feature Selection , 2005, IEEE Intell. Syst..
[118] Chee Keong Kwoh,et al. A Feature Subset Selection Method Based On High-Dimensional Mutual Information , 2011, Entropy.
[119] P. Jaccard. THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1 , 1912 .
[120] Josef Kittler,et al. Floating search methods in feature selection , 1994, Pattern Recognit. Lett..
[121] Qinghua Hu,et al. An efficient gene selection technique for cancer recognition based on neighborhood mutual information , 2010, Int. J. Mach. Learn. Cybern..
[122] Yue Han,et al. Stable Gene Selection from Microarray Data via Sample Weighting , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[123] Pan Su,et al. Feature Selection Ensemble , 2012, Turing-100.
[124] Feiping Nie,et al. Discriminative Least Squares Regression for Multiclass Classification and Feature Selection , 2012, IEEE Transactions on Neural Networks and Learning Systems.
[125] Jill P. Mesirov,et al. Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets , 2007, PloS one.
[126] Roliana Ibrahim,et al. Feature Reduction Using Standard Deviation with Different Subsets Selection in Sentiment Analysis , 2014, ACIIDS.
[127] N. Iizuka,et al. MECHANISMS OF DISEASE Mechanisms of disease , 2022 .
[128] Khalid Benabdeslem,et al. Efficient Semi-Supervised Feature Selection: Constraint, Relevance, and Redundancy , 2014, IEEE Transactions on Knowledge and Data Engineering.
[129] M. Ringnér,et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.
[130] Yihong Gong,et al. Feature Selection for Gene Expression Using Model-Based Entropy , 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[131] Ludmila I. Kuncheva,et al. A stability index for feature selection , 2007, Artificial Intelligence and Applications.
[132] Mohamed A. Ismail,et al. A novel ensemble selection method for cancer diagnosis using microarray datasets , 2012, 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE).
[133] W. Gerald,et al. Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy , 2005, Cancer.
[134] Yukyee Leung,et al. A Multiple-Filter-Multiple-Wrapper Approach to Gene Selection and Microarray Data Classification , 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[135] Dejan Juric,et al. Functional network analysis reveals extended gliomagenesis pathway maps and three novel MYC-interacting genes in human gliomas. , 2005, Cancer research.
[136] T. Poggio,et al. Multiclass cancer diagnosis using tumor gene expression signatures , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[137] Feiping Nie,et al. Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization , 2010, NIPS.
[138] Ron Shamir,et al. SlimPLS: A Method for Feature Selection in Gene Expression-Based Disease Classification , 2009, PloS one.
[139] Khalid Benabdeslem,et al. Constrained Laplacian Score for Semi-supervised Feature Selection , 2011, ECML/PKDD.
[140] Carla E. Brodley,et al. Feature Selection for Unsupervised Learning , 2004, J. Mach. Learn. Res..
[141] Deng Cai,et al. Unsupervised feature selection for multi-cluster data , 2010, KDD.
[142] Feng Yang,et al. Robust Feature Selection for Microarray Data Based on Multicriterion Fusion , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[143] Jing Deng,et al. An efficient two-stage gene selection method for microarray data , 2012 .
[144] E. Lander,et al. Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.
[145] Francesco Masulli,et al. Unsupervised Gene Selection and Clustering Using Simulated Annealing , 2005, WILF.
[146] Josef Kittler,et al. Pattern recognition : a statistical approach , 1982 .
[147] Nor Hayati Othman,et al. A review of feature selection techniques via gene expression profiles , 2008, 2008 International Symposium on Information Technology.
[148] Hong Peng,et al. Improving the Computational Efficiency of Recursive Cluster Elimination for Gene Selection , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[149] John T. Wei,et al. Integrative molecular concept modeling of prostate cancer progression , 2007, Nature Genetics.
[150] Ujjwal Maulik,et al. Fuzzy Preference Based Feature Selection and Semisupervised SVM for Cancer Classification , 2014, IEEE Transactions on NanoBioscience.
[151] Yong Xu,et al. Robust PCA based method for discovering differentially expressed genes , 2013, BMC Bioinformatics.
[152] Leslie S. Smith,et al. Feature subset selection in large dimensionality domains , 2010, Pattern Recognit..
[153] Lei Liu,et al. Ensemble gene selection by grouping for microarray data classification , 2010, J. Biomed. Informatics.
[154] Li-Yeh Chuang,et al. Improved binary PSO for feature selection using gene expression data , 2008, Comput. Biol. Chem..
[155] Sinisa Todorovic,et al. Local-Learning-Based Feature Selection for High-Dimensional Data Analysis , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[156] Mário A. T. Figueiredo,et al. Efficient feature selection filters for high-dimensional data , 2012, Pattern Recognit. Lett..
[157] B. Chandra,et al. An efficient statistical feature selection approach for classification of gene expression data , 2011, J. Biomed. Informatics.
[158] P. Brown,et al. Gene Selection in Arthritis Classification With Large-Scale Microarray Expression Profiles , 2003, Comparative and functional genomics.
[159] Manoranjan Dash,et al. Feature Selection for Clustering , 2009, Encyclopedia of Database Systems.
[160] Nir Friedman,et al. Tissue classification with gene expression profiles. , 2000 .
[161] Satoru Miyano,et al. A Top-r Feature Selection Algorithm for Microarray Gene Expression Data , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[162] Phayung Meesad,et al. Comparison of hybrid feature selection models on gene expression data , 2010, 2010 Eighth International Conference on ICT and Knowledge Engineering.