Feature selection methods for big data bioinformatics: A survey from the search perspective.

[1]  Sreejit Chakravarty,et al.  Microarray medical data classification using kernel ridge regression and modified cat swarm optimization based gene selection system , 2016, Swarm Evol. Comput..

[2]  M. Mohammadi,et al.  Robust and stable gene selection via Maximum-Minimum Correntropy Criterion. , 2016, Genomics.

[3]  Qi Zhu,et al.  A Class-Information-Based Sparse Component Analysis Method to Identify Differentially Expressed Genes on RNA-Seq Data , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[4]  L. Alberto Hernández Montiel,et al.  Hybrid Framework Using Multiple-Filters and an Embedded Approach for an Efficient Selection and Classification of Microarray Data , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[5]  V. K. Jayaraman,et al.  Identification of Glucose-Binding Pockets in Human Serum Albumin Using Support Vector Machine and Molecular Dynamics Simulations , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[6]  Rossitza Setchi,et al.  Feature selection using Joint Mutual Information Maximisation , 2015, Expert Syst. Appl..

[7]  Zhen Zhang,et al.  A Feature Selection Method for Prediction Essential Protein , 2015 .

[8]  Feiping Nie,et al.  Feature Selection via Global Redundancy Minimization , 2015, IEEE Transactions on Knowledge and Data Engineering.

[9]  Spiridon D. Likothanassis,et al.  YamiPred: A Novel Evolutionary Method for Predicting Pre-miRNAs and Selecting Relevant Features , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[10]  M. Schatz,et al.  Big Data: Astronomical or Genomical? , 2015, PLoS biology.

[11]  Yong Xu,et al.  RPCA-Based Tumor Classification Using Gene Expression Data , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[12]  Nestor Caticha,et al.  Supervised Variational Relevance Learning, An Analytic Geometric Feature Selection with Applications to Omic Datasets , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[13]  Ganesh R. Naik,et al.  Nonnegative Matrix Factorization for the Identification of EMG Finger Movements: Evaluation Using Matrix Analysis , 2015, IEEE Journal of Biomedical and Health Informatics.

[14]  Ilias Maglogiannis,et al.  Exploring Robust Diagnostic Signatures for Cutaneous Melanoma Utilizing Genetic and Imaging Data , 2015, IEEE Journal of Biomedical and Health Informatics.

[15]  Dimitrios I. Fotiadis,et al.  Machine learning applications in cancer prognosis and prediction , 2014, Computational and structural biotechnology journal.

[16]  Ao Li,et al.  Prediction of human disease-specific phosphorylation sites with combined feature selection approach and support vector machine , 2014, 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[17]  Simon Fong,et al.  Feature Selection in Life Science Classification: Metaheuristic Swarm Search , 2014, IT Professional.

[18]  David Correa Martins,et al.  A feature selection technique for inference of graphs from their known topological properties: Revealing scale-free gene regulatory networks , 2014, Inf. Sci..

[19]  Mike May,et al.  Big biological impacts from big data , 2014 .

[20]  S. Khan,et al.  Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces. , 2014, Journal of theoretical biology.

[21]  Ujjwal Maulik,et al.  Fuzzy Preference Based Feature Selection and Semisupervised SVM for Cancer Classification , 2014, IEEE Transactions on NanoBioscience.

[22]  Jim Jing-Yan Wang,et al.  Feature selection and multi-kernel learning for sparse representation on a manifold , 2014, Neural Networks.

[23]  Weisi Lin,et al.  Geometric Optimum Experimental Design for Collaborative Image Retrieval , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[24]  Amin Ahmadi Adl,et al.  Network-Based Methods to Identify Highly Discriminating Subsets of Biomarkers. , 2014, IEEE/ACM transactions on computational biology and bioinformatics.

[25]  Dinggang Shen,et al.  DNA Copy Number Selection Using Robust Structured Sparsity-Inducing Norms , 2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[26]  Minghao Yin,et al.  Multiobjective Binary Biogeography Based Optimization for Feature Selection Using Gene Expression Data , 2013, IEEE Transactions on NanoBioscience.

[27]  Maozhen Li,et al.  A MapReduce-based distributed SVM ensemble for scalable image classification and annotation , 2013, Comput. Math. Appl..

[28]  Partha Garai,et al.  On fuzzy-rough attribute selection: Criteria of Max-Dependency, Max-Relevance, Min-Redundancy, and Max-Significance , 2013, Appl. Soft Comput..

[29]  Ivor W. Tsang,et al.  Minimax Sparse Logistic Regression for Very High-Dimensional Feature Selection , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[30]  Thomas Martinetz,et al.  The Support Feature Machine: Classification with the Least Number of Features and Application to Neuroimaging Data , 2013, Neural Computation.

[31]  D. Liang,et al.  Comparison of Feature Selection Methods for Cross-Laboratory Microarray Analysis , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[32]  Shu-Yuan Chen,et al.  Classifying subtypes of acute lymphoblastic leukemia using silhouette statistics and genetic algorithms. , 2013, Gene.

[33]  Olcay Kursun,et al.  Multi objective SNP selection using pareto optimality , 2013, Comput. Biol. Chem..

[34]  Javier Pérez-Rodríguez,et al.  A scalable approach to simultaneous evolutionary instance and feature selection , 2013, Inf. Sci..

[35]  Dana Kulic,et al.  Feature-Selected Tree-Based Classification , 2013, IEEE Transactions on Cybernetics.

[36]  S. Anitha,et al.  SEMI-SUPERVISED BIASED MAXIMUM MARGIN ANALYSIS FOR INTERACTIVE IMAGE RETRIEVAL , 2013 .

[37]  Jagath C. Rajapakse,et al.  Multiclass Gene Selection Using Pareto-Fronts , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[38]  I. B. Ozyurt.  Automatic Identification and Classification of Noun Argument Structures in Biomedical Literature , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[39]  Hong Yan,et al.  Biomarker Identification and Cancer Classification Based on Microarray Data Using Laplace Naive Bayes Model with Mean Shrinkage , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[40]  M. Ng,et al.  SNP Selection and Classification of Genome-Wide SNP Data Using Stratified Sampling Random Forests , 2012, IEEE Transactions on NanoBioscience.

[41]  May D. Wang,et al.  Cardiovascular Genomics: A Biomarker Identification Pipeline , 2012, IEEE Transactions on Information Technology in Biomedicine.

[42]  Tiejun Tong,et al.  Gene Selection Using Iterative Feature Elimination Random Forests for Survival Outcomes , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[43]  Weisi Lin,et al.  Conjunctive Patches Subspace Learning With Side Information for Collaborative Image Retrieval , 2012, IEEE Transactions on Image Processing.

[44]  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.

[45]  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.

[46]  Ashfaqur Rahman,et al.  Cluster-Oriented Ensemble Classifier: Impact of Multicluster Characterization on Ensemble Classifier Learning , 2012, IEEE Transactions on Knowledge and Data Engineering.

[47]  Weisi Lin,et al.  Semisupervised Biased Maximum Margin Analysis for Interactive Image Retrieval , 2012, IEEE Transactions on Image Processing.

[48]  Weisi Lin,et al.  Generalized Biased Discriminant Analysis for Content-Based Image Retrieval , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[49]  Yue Han,et al.  Stable Gene Selection from Microarray Data via Sample Weighting , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[50]  Kerrie L. Mengersen,et al.  Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[51]  Jiuyong Li,et al.  Combined Feature Selection and Cancer Prognosis Using Support Vector Machine Regression , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[52]  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.

[53]  Jeffrey B. Endelman,et al.  Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP , 2011 .

[54]  Forbes J. Burkowski,et al.  Using Kernel Alignment to Select Features of Molecular Descriptors in a QSAR Study , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[55]  A. Esmaeili,et al.  Prediction of GABAA receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine. , 2011, Journal of theoretical biology.

[56]  Lei Zhang,et al.  Tumor Classification Based on Non-Negative Matrix Factorization Using Gene Expression Data , 2011, IEEE Transactions on NanoBioscience.

[57]  Jean Yee Hwa Yang,et al.  Two-Step Cross-Entropy Feature Selection for Microarrays—Power Through Complementarity , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[58]  Peng Zhang,et al.  Peak Tree: A New Tool for Multiscale Hierarchical Representation and Peak Detection of Mass Spectrometry Data , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[59]  Anirban Mukherjee,et al.  Cancer Classification from Gene Expression Data by NPPC Ensemble , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[60]  Kwong-Sak Leung,et al.  Data Mining on DNA Sequences of Hepatitis B Virus , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[61]  Hong Peng,et al.  Improving the Computational Efficiency of Recursive Cluster Elimination for Gene Selection , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[62]  Joaquim F. Pinto da Costa,et al.  A Weighted Principal Component Analysis and Its Application to Gene Expression Data , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[63]  Sushmita Mitra,et al.  Genetic Networks and Soft Computing , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[64]  Lipo Wang,et al.  Optimal location management in mobile computing with hybrid genetic algorithm and particle swarm optimization (GA-PSO) , 2010, 2010 17th IEEE International Conference on Electronics, Circuits and Systems.

[65]  Xue-wen Chen,et al.  Combating the Small Sample Class Imbalance Problem Using Feature Selection , 2010, IEEE Transactions on Knowledge and Data Engineering.

[66]  Ming Zhu,et al.  Intelligent trading using support vector regression and multilayer perceptrons optimized with genetic algorithms , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[67]  Jacek M. Zurada,et al.  Identification of Full and Partial Class Relevant Genes , 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[68]  Ailsa H. Land,et al.  An Automatic Method of Solving Discrete Programming Problems , 1960 .

[69]  Shili Lin,et al.  Sparse Support Vector Machines with L_{p} Penalty for Biomarker Identification , 2010, TCBB.

[70]  Kai Yu,et al.  Feature Selection for Gene Expression Using Model-Based Entropy , 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[71]  S. Niijima,et al.  Laplacian Linear Discriminant Analysis Approach to Unsupervised Feature Selection , 2009, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[72]  Limsoon Wong,et al.  Key node selection for containing infectious disease spread using particle swarm optimization , 2009, 2009 IEEE Swarm Intelligence Symposium.

[73]  Hsueh-Wei Chang,et al.  A two-stage feature selection method for gene expression data. , 2009, Omics : a journal of integrative biology.

[74]  Pradipta Maji,et al.  $f$-Information Measures for Efficient Selection of Discriminative Genes From Microarray Data , 2009, IEEE Transactions on Biomedical Engineering.

[75]  Lipo Wang,et al.  Feature Selection Based on the Rough Set Theory and Expectation-Maximization Clustering Algorithm , 2008, RSCTC.

[76]  Lipo Wang,et al.  Ant Colony Optimization for the Traveling Salesman Problem Based on Ants with Memory , 2008, 2008 Fourth International Conference on Natural Computation.

[77]  Feng Chu,et al.  A General Wrapper Approach to Selection of Class-Dependent Features , 2008, IEEE Transactions on Neural Networks.

[78]  Wen Liu,et al.  Noisy Chaotic Neural Networks With Variable Thresholds for the Frequency Assignment Problem in Satellite Communications , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[79]  Lipo Wang,et al.  A Modified T-test Feature Selection Method and Its Application on the HapMap Genotype Data , 2008, Genom. Proteom. Bioinform..

[80]  Lipo Wang,et al.  Effective selection of informative SNPs and classification on the HapMap genotype data , 2007, BMC Bioinformatics.

[81]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[82]  Senjian An,et al.  Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression , 2007, Pattern Recognit..

[83]  Yanqing Zhang,et al.  Development of Two-Stage SVM-RFE Gene Selection Strategy for Microarray Expression Data Analysis , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[84]  Li Wang,et al.  Hybrid huberized support vector machines for microarray classification and gene selection , 2008, Bioinform..

[85]  Chuan Yi Tang,et al.  Feature Selection and Combination Criteria for Improving Accuracy in Protein Structure Prediction , 2007, IEEE Transactions on NanoBioscience.

[86]  Sankar K. Pal,et al.  Rough-Fuzzy C-Medoids Algorithm and Selection of Bio-Basis for Amino Acid Sequence Analysis , 2007, IEEE Transactions on Knowledge and Data Engineering.

[87]  G. Bontempi,et al.  A Blocking Strategy to Improve Gene Selection for Classification of Gene Expression Data , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[88]  U. Braga-Neto,et al.  Fads and fallacies in the name of small-sample microarray classification - A highlight of misunderstanding and erroneous usage in the applications of genomic signal processing , 2007, IEEE Signal Processing Magazine.

[89]  Wei Xie,et al.  Accurate Cancer Classification Using Expressions of Very Few Genes , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[90]  Louise C. Showe,et al.  Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data , 2007, BMC Bioinformatics.

[91]  Cesare Furlanello,et al.  Combining feature selection and DTW for time-varying functional genomics , 2006, IEEE Transactions on Signal Processing.

[92]  Bing Liu,et al.  An efficient semi-unsupervised gene selection method via spectral biclustering , 2006, IEEE Transactions on NanoBioscience.

[93]  Hiroshi Sako,et al.  Class-specific feature polynomial classifier for pattern classification and its application to handwritten numeral recognition , 2006, Pattern Recognit..

[94]  Lipo Wang,et al.  Data Mining With Computational Intelligence , 2006, IEEE Transactions on Neural Networks.

[95]  Lei Zhou,et al.  FPGA segmented channel routing using genetic algorithms , 2005, 2005 IEEE Congress on Evolutionary Computation.

[96]  Feng Chu,et al.  Applications of support vector machines to cancer classification with microarray data , 2005, Int. J. Neural Syst..

[97]  Gabriele Steidl,et al.  Combined SVM-Based Feature Selection and Classification , 2005, Machine Learning.

[98]  F. Azuaje,et al.  Multiple SVM-RFE for gene selection in cancer classification with expression data , 2005, IEEE Transactions on NanoBioscience.

[99]  Chunru Wan,et al.  Classification using support vector machines with graded resolution , 2005, 2005 IEEE International Conference on Granular Computing.

[100]  W. Fung,et al.  Detecting differentially expressed genes by relative entropy. , 2005, Journal of theoretical biology.

[101]  Lipo Wang,et al.  A Simple Rule Extraction Method Using a Compact RBF Neural Network , 2005, ISNN.

[102]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[103]  S. Merler,et al.  Semisupervised learning for molecular profiling , 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[104]  Xiaoxing Liu,et al.  An Entropy-based gene selection method for cancer classification using microarray data , 2005, BMC Bioinformatics.

[105]  Ilya Levner,et al.  Feature selection and nearest centroid classification for protein mass spectrometry , 2005, BMC Bioinformatics.

[106]  Mike P. Liang,et al.  Computational functional genomics , 2004, IEEE Signal Process. Mag..

[107]  Lipo Wang,et al.  A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[108]  R. Tibshirani,et al.  Efficient quadratic regularization for expression arrays. , 2004, Biostatistics.

[109]  Boon-Hee Soong,et al.  Broadcast scheduling in packet radio networks using mixed tabu-greedy algorithm , 2004 .

[110]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[111]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[112]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[113]  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.

[114]  Lipo Wang,et al.  Gene expression data analysis using support vector machines , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[115]  Lipo Wang,et al.  Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[116]  Paul M. Baggenstoss.  The PDF projection theorem and the class-specific method , 2003, IEEE Trans. Signal Process..

[117]  L. Wang,et al.  A Mixed Branch-and-bound and Neural Network Approach for the Broadcast Scheduling Problem , 2003, HIS.

[118]  Thomas R. Ioerger,et al.  Enhancing Learning using Feature and Example selection , 2003 .

[119]  Lipo Wang,et al.  Training RBF neural networks on unbalanced data , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[120]  Lipo Wang,et al.  Genetic algorithms for optimal channel assignment in mobile communications , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[121]  Jesús S. Aguilar-Ruiz,et al.  SOAP: Efficient Feature Selection of Numeric Attributes , 2002, IBERAMIA.

[122]  Carsten O. Daub,et al.  The mutual information: Detecting and evaluating dependencies between variables , 2002, ECCB.

[123]  N. Xiong,et al.  A hybrid approach to input selection for complex processes , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[124]  Lipo Wang,et al.  A GA-based RBF classifier with class-dependent features , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[125]  Lipo Wang,et al.  Rule extraction from an RBF classifier based on class-dependent features , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[126]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[127]  Thomas A. Darden,et al.  Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method , 2001, Bioinform..

[128]  Lipo Wang,et al.  Linguistic Rule Extraction From a Simplified RBF Neural Network , 2001, Comput. Stat..

[129]  W. Malina,et al.  Two-parameter Fisher criterion , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[130]  Bruce A. Draper,et al.  Feature selection from huge feature sets , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[131]  J. Thomas,et al.  An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. , 2001, Genome research.

[132]  Lipo Wang,et al.  Rule extraction by genetic algorithms based on a simplified RBF neural network , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[133]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[134]  Anil K. Jain,et al.  Dimensionality reduction using genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[135]  Gary A. Churchill,et al.  Analysis of Variance for Gene Expression Microarray Data , 2000, J. Comput. Biol..

[136]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[137]  Ching Y. Suen,et al.  Analysis of Class Separation and Combination of Class-Dependent Features for Handwriting Recognition , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[138]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[139]  Il-Seok Oh,et al.  Using class separation for feature analysis and combination of class-dependent features , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[140]  Alexander Gammerman,et al.  Ridge Regression Learning Algorithm in Dual Variables , 1998, ICML.

[141]  Kate Smith-Miles,et al.  On chaotic simulated annealing , 1998, IEEE Trans. Neural Networks.

[142]  Igor Kononenko,et al.  Cost-Sensitive Learning with Neural Networks , 1998, ECAI.

[143]  Daphne Koller,et al.  Toward Optimal Feature Selection , 1996, ICML.

[144]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[145]  Kazuyuki Aihara,et al.  Chaotic simulated annealing by a neural network model with transient chaos , 1995, Neural Networks.

[146]  Shyang Chang,et al.  An adaptive learning algorithm for principal component analysis , 1995, IEEE Trans. Neural Networks.

[147]  Pat Langley,et al.  Elements of Machine Learning , 1995 .

[148]  Pat Langley,et al.  Induction of Selective Bayesian Classifiers , 1994, UAI.

[149]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

[150]  Dejan J. Sobajic,et al.  Learning and generalization characteristics of the random vector Functional-link net , 1994, Neurocomputing.

[151]  Larry A. Rendell,et al.  The Feature Selection Problem: Traditional Methods and a New Algorithm , 1992, AAAI.

[152]  Hiroshi Nozawa,et al.  A neural network model as a globally coupled map and applications based on chaos. , 1992, Chaos.

[153]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[154]  Jack Sklansky,et al.  A note on genetic algorithms for large-scale feature selection , 1989, Pattern Recognit. Lett..

[155]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[156]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[157]  Fred W. Glover,et al.  Future paths for integer programming and links to artificial intelligence , 1986, Comput. Oper. Res..

[158]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[159]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[160]  A. Wayne Whitney,et al.  A Direct Method of Nonparametric Measurement Selection , 1971, IEEE Transactions on Computers.

[161]  Thomas Marill,et al.  On the effectiveness of receptors in recognition systems , 1963, IEEE Trans. Inf. Theory.