Random Forests with ensemble of feature spaces

[1]  Yunming Ye,et al.  Stratified sampling for feature subspace selection in random forests for high dimensional data , 2013, Pattern Recognit..

[2]  Ullrich Köthe,et al.  On Oblique Random Forests , 2011, ECML/PKDD.

[3]  Koen W. De Bock,et al.  An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction , 2011, Expert Syst. Appl..

[4]  Shuiping Gou,et al.  Greedy optimization classifiers ensemble based on diversity , 2011, Pattern Recognit..

[5]  Xudong Jiang,et al.  Linear Subspace Learning-Based Dimensionality Reduction , 2011, IEEE Signal Processing Magazine.

[6]  P. Suganthan,et al.  AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. , 2011, Journal of theoretical biology.

[7]  Ethem Alpaydin,et al.  Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..

[8]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[9]  P. Suganthan,et al.  SMpred: A Support Vector Machine Approach to Identify Structural Motifs in Protein Structure Without Using Evolutionary Information , 2010, Journal of biomolecular structure & dynamics.

[10]  George C. Runger,et al.  Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination , 2009, J. Mach. Learn. Res..

[11]  Hansheng Wang,et al.  Subgroup Analysis via Recursive Partitioning , 2009, J. Mach. Learn. Res..

[12]  Bjoern H. Menze,et al.  A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data , 2009, BMC Bioinformatics.

[13]  Daniel Hernández-Lobato,et al.  An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Yvan Saeys,et al.  Robust Feature Selection Using Ensemble Feature Selection Techniques , 2008, ECML/PKDD.

[15]  William G. Hanley,et al.  Discriminant Random Forests , 2008, DMIN.

[16]  Chong Jin Ong,et al.  A Feature Selection Method for Multilevel Mental Fatigue EEG Classification , 2007, IEEE Transactions on Biomedical Engineering.

[17]  Juan José Rodríguez Diez,et al.  An Experimental Study on Rotation Forest Ensembles , 2007, MCS.

[18]  James C. Spall,et al.  Introduction to Stochastic Search and Optimization. Estimation, Simulation, and Control (Spall, J.C. , 2007 .

[19]  Bjoern H Menze,et al.  Multivariate feature selection and hierarchical classification for infrared spectroscopy: serum-based detection of bovine spongiform encephalopathy , 2007, Analytical and bioanalytical chemistry.

[20]  Juan José Rodríguez Diez,et al.  Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[22]  Mahesh Pal,et al.  Random forest classifier for remote sensing classification , 2005 .

[23]  Torsten Hothorn,et al.  Bundling Classifiers by Bagging Trees , 2002, Comput. Stat. Data Anal..

[24]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[25]  Marko Robnik-Sikonja,et al.  Improving Random Forests , 2004, ECML.

[26]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[27]  Jun Chen,et al.  Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes , 2004, BMC Bioinformatics.

[28]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[29]  Stanislav KolenikovGustavo Angeles The Use of Discrete Data in PCA: Theory, Simulations, and Applications to Socioeconomic Indices , 2004 .

[30]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[31]  Huiqing Liu,et al.  An in-silico method for prediction of polyadenylation signals in human sequences. , 2003, Genome informatics. International Conference on Genome Informatics.

[32]  D. Gautheret,et al.  Sequence determinants in human polyadenylation site selection , 2003, BMC Genomics.

[33]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[34]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[35]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[37]  Robert P. W. Duin,et al.  The Role of Combining Rules in Bagging and Boosting , 2000, SSPR/SPR.

[38]  Thomas G. Dietterich Ensemble Methods in Machine Learning , 2000, Multiple Classifier Systems.

[39]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[41]  Thomas G. Dietterich,et al.  Pruning Adaptive Boosting , 1997, ICML.

[42]  Harry Wechsler,et al.  Face recognition using hybrid classifier systems , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[43]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[44]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[45]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[46]  John W. Sammon,et al.  An Optimal Set of Discriminant Vectors , 1975, IEEE Transactions on Computers.

[47]  Josef Kittler,et al.  A new approach to feature selection based on the Karhunen-Loeve expansion , 1973, Pattern Recognit..