Progressive subspace ensemble learning
暂无分享,去创建一个
Jane You | Zhiwen Yu | Jun Zhang | Guoqiang Han | Hau-San Wong | Si Wu | Daxing Wang | Guoqiang Han | J. You | Zhiwen Yu | H. Wong | Si Wu | Jun Zhang | Daxing Wang | Hau-San Wong
[1] Jane You,et al. From cluster ensemble to structure ensemble , 2012, Inf. Sci..
[2] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[3] Robert Sabourin,et al. An adaptive ensemble of fuzzy ARTMAP neural networks for video-based face classification , 2010, IEEE Congress on Evolutionary Computation.
[4] Juan José Rodríguez Diez,et al. Random Subspace Ensembles for fMRI Classification , 2010, IEEE Transactions on Medical Imaging.
[5] Daniel Hernández-Lobato,et al. An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] Zhiwen Yu,et al. Class Discovery From Gene Expression Data Based on Perturbation and Cluster Ensemble , 2009, IEEE Transactions on NanoBioscience.
[7] Zhiwen Yu,et al. Graph-based consensus clustering for class discovery from gene expression data , 2007, Bioinform..
[8] Witold Pedrycz,et al. A Study on Relationship Between Generalization Abilities and Fuzziness of Base Classifiers in Ensemble Learning , 2015, IEEE Transactions on Fuzzy Systems.
[9] Qinghua Hu,et al. EROS: Ensemble rough subspaces , 2007, Pattern Recognit..
[10] Ludmila I. Kuncheva,et al. "Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting , 2003, IEEE Trans. Fuzzy Syst..
[11] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[12] Francis K. H. Quek,et al. Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets , 2003, Pattern Recognit..
[13] S. García,et al. An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons , 2008 .
[14] Ponnuthurai N. Suganthan,et al. Oblique Decision Tree Ensemble via Multisurface Proximal Support Vector Machine , 2015, IEEE Transactions on Cybernetics.
[15] Jane You,et al. Hybrid Fuzzy Cluster Ensemble Framework for Tumor Clustering from Biomolecular Data , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[16] Eyke Hüllermeier,et al. Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting , 2010, Pattern Recognit..
[17] Fang Liu,et al. A novel dynamic rough subspace based selective ensemble , 2015, Pattern Recognit..
[18] Jane You,et al. SC³: Triple Spectral Clustering-Based Consensus Clustering Framework for Class Discovery from Cancer Gene Expression Profiles , 2012, TCBB.
[19] ZhouZhi-Hua,et al. Ensembling neural networks , 2002 .
[20] Geoffrey I. Webb,et al. MultiBoosting: A Technique for Combining Boosting and Wagging , 2000, Machine Learning.
[21] Fabio Roli,et al. Intrusion detection in computer networks by a modular ensemble of one-class classifiers , 2008, Inf. Fusion.
[22] Jane You,et al. Semi-supervised classification based on random subspace dimensionality reduction , 2012, Pattern Recognit..
[23] Lei Liu,et al. Ensemble gene selection for cancer classification , 2010, Pattern Recognit..
[24] Alberto Maria Segre,et al. Programs for Machine Learning , 1994 .
[25] David Vázquez,et al. Occlusion Handling via Random Subspace Classifiers for Human Detection , 2014, IEEE Transactions on Cybernetics.
[26] Gavin Brown,et al. Learn++.MF: A random subspace approach for the missing feature problem , 2010, Pattern Recognit..
[27] Juan José Rodríguez Diez,et al. Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] Horst Bunke,et al. Feature selection algorithms for the generation of multiple classifier systems and their application to handwritten word recognition , 2004 .
[29] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[30] Mohamed S. Kamel,et al. Integrating Heterogeneous Classifier Ensembles for EMG Signal Decomposition Based on Classifier Agreement , 2010, IEEE Transactions on Information Technology in Biomedicine.
[31] Yongsheng Ding,et al. An ensemble classifier based prediction of G-protein-coupled receptor classes in low homology , 2015, Neurocomputing.
[32] Han Guoqiang,et al. SC(3): Triple spectral clustering-based consensus clustering framework for class discovery from cancer gene expression profiles. , 2012, IEEE/ACM transactions on computational biology and bioinformatics.
[33] P. N. Suganthan,et al. A comprehensive evaluation of random vector functional link networks , 2016, Inf. Sci..
[34] Xuelong Li,et al. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[35] Mohammad Reza Daliri,et al. Predicting the Cognitive States of the Subjects in Functional Magnetic Resonance Imaging Signals Using the Combination of Feature Selection Strategies , 2011, Brain Topography.
[36] H WittenIan,et al. The WEKA data mining software , 2009 .
[37] Ludmila I. Kuncheva,et al. A Bound on Kappa-Error Diagrams for Analysis of Classifier Ensembles , 2013, IEEE Transactions on Knowledge and Data Engineering.
[38] Zhiwen Yu,et al. Knowledge Based Cluster Ensemble for Cancer Discovery From Biomolecular Data , 2011, IEEE Transactions on NanoBioscience.
[39] Ali S. Hadi,et al. Finding Groups in Data: An Introduction to Chster Analysis , 1991 .
[40] M.F. Saeedian,et al. Spam Detection Using Dynamic Weighted Voting Based on Clustering , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.
[41] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[42] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[43] Hareton K. N. Leung,et al. Incremental Semi-Supervised Clustering Ensemble for High Dimensional Data Clustering , 2016, IEEE Transactions on Knowledge and Data Engineering.
[44] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[45] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[46] L. Kuncheva. ‘ Fuzzy ’ vs ‘ Non-fuzzy ’ in Combining Classifiers Designed by Boosting , 2003 .
[47]
Hareton K. N. Leung,et al.
Hybrid
[48] Francisco Herrera,et al. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[49] Mohammad Reza Daliri. Feature selection using binary particle swarm optimization and support vector machines for medical diagnosis , 2012, Biomedizinische Technik. Biomedical engineering.
[50] Zhiwen Yu,et al. Adaptive noise immune cluster ensemble using affinity propagation , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).
[51] Robert Sabourin,et al. A dynamic overproduce-and-choose strategy for the selection of classifier ensembles , 2008, Pattern Recognit..
[52] Zhiwen Yu,et al. Hybrid Adaptive Classifier Ensemble , 2015, IEEE Transactions on Cybernetics.
[53] Juan José Rodríguez Diez,et al. Classifier Ensembles with a Random Linear Oracle , 2007, IEEE Transactions on Knowledge and Data Engineering.
[54] Liying Yang,et al. Classifiers selection for ensemble learning based on accuracy and diversity , 2011 .
[55] Yunde Jia,et al. A linear discriminant analysis framework based on random subspace for face recognition , 2007, Pattern Recognit..
[56] Alexander Schliep,et al. Clustering cancer gene expression data: a comparative study , 2008, BMC Bioinformatics.
[57] Loris Nanni,et al. Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification , 2015, Comput. Intell. Neurosci..
[58] Wei Tang,et al. Ensembling neural networks: Many could be better than all , 2002, Artif. Intell..
[59] Safdar Ali,et al. Can-Evo-Ens: Classifier stacking based evolutionary ensemble system for prediction of human breast cancer using amino acid sequences , 2015, J. Biomed. Informatics.
[60] HerreraFrancisco,et al. A Review on Ensembles for the Class Imbalance Problem , 2012 .
[61] Mohammad Reza Daliri,et al. Combining extreme learning machines using support vector machines for breast tissue classification , 2015, Computer methods in biomechanics and biomedical engineering.
[62] H. J. Mclaughlin,et al. Learn , 2002 .
[63] Ajith Abraham,et al. A novel multiplex cascade classifier for pedestrian detection , 2013, Pattern Recognit. Lett..
[64] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[65] Rui Liu,et al. Chinese Text Classification Based on the BVB Model , 2008, 2008 Fourth International Conference on Semantics, Knowledge and Grid.
[66] Shiliang Sun,et al. The Selective Random Subspace Predictor for Traffic Flow Forecasting , 2007, IEEE Transactions on Intelligent Transportation Systems.
[67] George D. C. Cavalcanti,et al. META-DES: A dynamic ensemble selection framework using meta-learning , 2015, Pattern Recognit..
[68] Chun-Xia Zhang,et al. RotBoost: A technique for combining Rotation Forest and AdaBoost , 2008, Pattern Recognit. Lett..
[69] Loris Nanni,et al. Double committee adaboost , 2013 .
[70] Xiaoyi Jiang,et al. A dynamic classifier ensemble selection approach for noise data , 2010, Inf. Sci..
[71] Lie Guo,et al. Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine , 2012, Expert Syst. Appl..
[72] Ponnuthurai N. Suganthan,et al. Random Forests with ensemble of feature spaces , 2014, Pattern Recognit..
[73] 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.
[74] Daoqiang Zhang,et al. A novel ensemble construction method for multi-view data using random cross-view correlation between within-class examples , 2011, Pattern Recognit..
[75] Yunming Ye,et al. Stratified sampling for feature subspace selection in random forests for high dimensional data , 2013, Pattern Recognit..
[76] Ludmila I. Kuncheva,et al. Naive random subspace ensemble with linear classifiers for real-time classification of fMRI data , 2012, Pattern Recognit..
[77] Zhi-Hua Zhou,et al. NeC4.5: Neural Ensemble Based C4.5 , 2004, IEEE Trans. Knowl. Data Eng..
[78] Mohammad Reza Daliri,et al. Supervised segmentation of MRI brain images using combination of multiple classifiers , 2015, Australasian Physical & Engineering Sciences in Medicine.
[79] Francisco Herrera,et al. EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling , 2013, Pattern Recognit..
[80] David W. Aha,et al. Instance-Based Learning Algorithms , 1991, Machine Learning.