Kernel-based learning and feature selection analysis for cancer diagnosis
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Abdelkader Benyettou | Seyyid Ahmed Medjahed | Mohammed Ouali | Tamazouzt Ait Saadi | A. Benyettou | M. Ouali | S. A. Medjahed | T. A. Saadi
[1] W ReynoldsCraig. Flocks, herds and schools: A distributed behavioral model , 1987 .
[2] Hossein Nezamabadi-pour,et al. GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..
[3] Ali Akbar Abdoos,et al. Combined VMD-SVM based feature selection method for classification of power quality events , 2016, Appl. Soft Comput..
[4] Enrique Alba,et al. Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments , 2016, Appl. Soft Comput..
[5] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[6] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[7] A. Jemal,et al. Cancer treatment and survivorship statistics, 2016 , 2016, CA: a cancer journal for clinicians.
[8] J.C. Rajapakse,et al. SVM-RFE With MRMR Filter for Gene Selection , 2010, IEEE Transactions on NanoBioscience.
[9] Ying Liu,et al. A Hybrid Approach for Biomarker Discovery from Microarray Gene Expression Data for Cancer Classification , 2007, Cancer informatics.
[10] Razieh Sheikhpour,et al. Particle swarm optimization for bandwidth determination and feature selection of kernel density estimation based classifiers in diagnosis of breast cancer , 2016, Appl. Soft Comput..
[11] Gavin Brown,et al. Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection , 2012, J. Mach. Learn. Res..
[12] Muchenxuan Tong,et al. An ensemble of SVM classifiers based on gene pairs , 2013, Comput. Biol. Medicine.
[13] E. Petricoin,et al. Use of proteomic patterns in serum to identify ovarian cancer , 2002, The Lancet.
[14] Yudong D. He,et al. Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.
[15] 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.
[16] A. Jemal,et al. Cancer treatment and survivorship statistics, 2012 , 2012, CA: a cancer journal for clinicians.
[17] 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.
[18] Melanie Hilario,et al. Stability of feature selection algorithms , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).
[19] Jin-Kao Hao,et al. Gene Selection for Microarray Data by a LDA-Based Genetic Algorithm , 2008, PRIB.
[20] Abdelkader Benyettou,et al. Gray Wolf Optimizer for hyperspectral band selection , 2016, Appl. Soft Comput..
[21] Daniel Q. Naiman,et al. Simple decision rules for classifying human cancers from gene expression profiles , 2005, Bioinform..
[22] Craig W. Reynolds. Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.
[23] Aik Choon Tan,et al. Ensemble machine learning on gene expression data for cancer classification. , 2003, Applied bioinformatics.
[24] Alexander Isaev,et al. PyEvolve: a toolkit for statistical modelling of molecular evolution , 2004, BMC Bioinformatics.
[25] Othman Soufan,et al. An Empirical Study of Wrappers for Feature Subset Selection based on a Parallel Genetic Algorithm: The Multi-Wrapper Model , 2012 .
[26] Beatriz A. Garro,et al. Classification of DNA microarrays using artificial neural networks and ABC algorithm , 2016, Appl. Soft Comput..
[27] García-PedrajasNicolás,et al. Simultaneous instance and feature selection and weighting using evolutionary computation , 2015 .
[28] Xin-She Yang,et al. Binary bat algorithm , 2013, Neural Computing and Applications.
[29] Kuanquan Wang,et al. Informative Gene Selection and Tumor Classification by Null Space LDA for Microarray Data , 2007, ESCAPE.
[30] Jin-Kao Hao,et al. A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data , 2006, EvoWorkshops.
[31] Huowang Chen,et al. Feature Extraction from Tumor Gene Expression Profiles Using DCT and DFT , 2007, EPIA Workshops.
[32] D. Dai,et al. Generalized Discriminant Analysis for Tumor Classification with Gene Expression Data , 2006, 2006 International Conference on Machine Learning and Cybernetics.
[33] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.
[34] Jieping Ye,et al. Using uncorrelated discriminant analysis for tissue classification with gene expression data , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[35] Sung-Bae Cho,et al. Cancer classification using ensemble of neural networks with multiple significant gene subsets , 2007, Applied Intelligence.
[36] Zne-Jung Lee,et al. Parameter determination of support vector machine and feature selection using simulated annealing approach , 2008, Appl. Soft Comput..
[37] Shaoning Pang,et al. Classification consistency analysis for bootstrapping gene selection , 2007, Neural Computing and Applications.
[38] Javier Pérez-Rodríguez,et al. Simultaneous instance and feature selection and weighting using evolutionary computation: Proposal and study , 2015, Appl. Soft Comput..
[39] Zhoujun Li,et al. An Effective Gene Selection Method Based on Relevance Analysis and Discernibility Matrix , 2007, PAKDD.
[40] Bernhard Schölkopf,et al. New Support Vector Algorithms , 2000, Neural Computation.
[41] Yong Xu,et al. Neuro-Fuzzy Ensemble Approach for Microarray Cancer Gene Expression Data Analysis , 2006, 2006 International Symposium on Evolving Fuzzy Systems.
[42] Shih-Wei Lin,et al. Particle swarm optimization for parameter determination and feature selection of support vector machines , 2008, Expert Syst. Appl..
[43] Riccardo Poli,et al. Particle swarm optimization , 1995, Swarm Intelligence.
[44] V. Bajic,et al. DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm , 2015, PloS one.
[45] Ash A. Alizadeh,et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.
[46] Shutao Li,et al. Gene selection using genetic algorithm and support vectors machines , 2008, Soft Comput..
[47] Chris H. Q. Ding,et al. Minimum Redundancy Feature Selection from Microarray Gene Expression Data , 2005, J. Bioinform. Comput. Biol..
[48] 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.
[49] Jie Duan,et al. Multi-label feature selection based on neighborhood mutual information , 2016, Appl. Soft Comput..
[50] M. Johnson,et al. Circulating microRNAs in Sera Correlate with Soluble Biomarkers of Immune Activation but Do Not Predict Mortality in ART Treated Individuals with HIV-1 Infection: A Case Control Study , 2015, PloS one.
[51] Jack Y. Yang,et al. Partial Least Squares Based Dimension Reduction with Gene Selection for Tumor Classification , 2007, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering.
[52] Tianzi Jiang,et al. A combinational feature selection and ensemble neural network method for classification of gene expression data , 2004, BMC Bioinformatics.
[53] Seyedali Mirjalili,et al. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems , 2015, Neural Computing and Applications.
[54] Johan A. K. Suykens,et al. Least Squares Support Vector Machines , 2002 .