INCREMENTAL CONSTRUCTION OF COST-CONSCIOUS ENSEMBLES USING MULTIPLE LEARNERS AND REPRESENTATIONS IN MACHINE LEARNING

[1]  Josef Kittler,et al.  Experimental evaluation of expert fusion strategies , 1999, Pattern Recognit. Lett..

[2]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[3]  Bülent Sankur,et al.  Representation Plurality and Fusion for 3-D Face Recognition , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Lior Rokach,et al.  Selective Voting - Getting More for Less in Sensor Fusion , 2006, Int. J. Pattern Recognit. Artif. Intell..

[5]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.

[6]  S. Y. Sohn,et al.  Experimental study for the comparison of classifier combination methods , 2007, Pattern Recognit..

[7]  Kevin W. Bowyer,et al.  Combination of Multiple Classifiers Using Local Accuracy Estimates , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Ian H. Witten,et al.  Issues in Stacked Generalization , 2011, J. Artif. Intell. Res..

[9]  Filippo Menczer,et al.  Meta-evolutionary ensembles , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[10]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .

[11]  Berk Gökberk,et al.  3D shape-based face representation and feature extraction for face recognition , 2006, Image Vis. Comput..

[12]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

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

[14]  Johannes Fürnkranz,et al.  An Evaluation of Grading Classifiers , 2001, IDA.

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

[16]  Thomas G. Dietterich Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[17]  Marc Acheroy,et al.  Automatic 3D face authentication , 2000, Image Vis. Comput..

[18]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Ethem Alpaydin,et al.  MultiStage Cascading of Multiple Classifiers: One Man's Noise is Another Man's Data , 2000, ICML.

[20]  Arun Ross,et al.  Score normalization in multimodal biometric systems , 2005, Pattern Recognit..

[21]  I. Jolliffe Discarding Variables in a Principal Component Analysis. Ii: Real Data , 1973 .

[22]  Fabio Roli,et al.  A theoretical and experimental analysis of linear combiners for multiple classifier systems , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Noel E. Sharkey,et al.  The "Test and Select" Approach to Ensemble Combination , 2000, Multiple Classifier Systems.

[24]  Fabio Roli,et al.  Methods for Designing Multiple Classifier Systems , 2001, Multiple Classifier Systems.

[25]  Naonori Ueda,et al.  Optimal Linear Combination of Neural Networks for Improving Classification Performance , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  William B. Yates,et al.  Engineering Multiversion Neural-Net Systems , 1996, Neural Computation.

[27]  Ethem Alpaydin,et al.  Linear Discriminant Trees , 2000, ICML.

[28]  C. Kaynak,et al.  Techniques for Combining Multiple Learners , 1998 .

[29]  Bogdan Gabrys,et al.  Classifier selection for majority voting , 2005, Inf. Fusion.

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

[31]  Stephen D. Bay Nearest neighbor classification from multiple feature subsets , 1999, Intell. Data Anal..

[32]  Berk Gökberk,et al.  3D shape-based face recognition using automatically registered facial surfaces , 2004, ICPR 2004.

[33]  Robert P. W. Duin,et al.  Is independence good for combining classifiers? , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[34]  João Gama,et al.  Combining Classifiers by Constructive Induction , 1998, ECML.

[35]  A. C. Rencher Interpretation of Canonical Discriminant Functions, Canonical Variates, and Principal Components , 1992 .

[36]  Ethem Alpaydin,et al.  Voting over Multiple Condensed Nearest Neighbors , 1997, Artificial Intelligence Review.

[37]  H. Akaike A new look at the statistical model identification , 1974 .

[38]  Ludmila I. Kuncheva,et al.  A Theoretical Study on Six Classifier Fusion Strategies , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

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

[40]  Ethem Alpaydin,et al.  Incremental construction of classifier and discriminant ensembles , 2009, Inf. Sci..

[41]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[42]  William Nick Street,et al.  Ensemble Pruning Via Semi-definite Programming , 2006, J. Mach. Learn. Res..

[43]  Peter D. Turney Types of Cost in Inductive Concept Learning , 2002, ArXiv.

[44]  Josef Kittler,et al.  Combining multiple classifiers by averaging or by multiplying? , 2000, Pattern Recognit..

[45]  Tom Heskes,et al.  Clustering ensembles of neural network models , 2003, Neural Networks.

[46]  Leo Breiman,et al.  Prediction Games and Arcing Algorithms , 1999, Neural Computation.

[47]  Xin Yao,et al.  A constructive algorithm for training cooperative neural network ensembles , 2003, IEEE Trans. Neural Networks.

[48]  Ethem Alpaydin,et al.  Combining multiple representations and classifiers for pen-based handwritten digit recognition , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[49]  João B. D. Cabrera,et al.  On the impact of fusion strategies on classification errors for large ensembles of classifiers , 2006, Pattern Recognit..

[50]  Juan José Rodríguez Diez,et al.  Classifier Ensembles with a Random Linear Oracle , 2007, IEEE Transactions on Knowledge and Data Engineering.

[51]  Sarunas Raudys,et al.  Trainable fusion rules. I. Large sample size case , 2006, Neural Networks.

[52]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[53]  Christopher J. Merz,et al.  Using Correspondence Analysis to Combine Classifiers , 1999, Machine Learning.

[54]  Thomas G. Dietterich An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[55]  Belur V. Dasarathy A special issue on diversity in multiple classifier systems , 2005, Inf. Fusion.

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

[57]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[58]  Robert P. W. Duin,et al.  The combining classifier: to train or not to train? , 2002, Object recognition supported by user interaction for service robots.

[59]  Salvatore J. Stolfo,et al.  Cost Complexity-Based Pruning of Ensemble Classifiers , 2001, Knowledge and Information Systems.

[60]  Wei Tang,et al.  Ensembling neural networks: Many could be better than all , 2002, Artif. Intell..

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

[62]  Geoffrey I. Webb,et al.  To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators , 2007, IEEE Transactions on Knowledge and Data Engineering.

[63]  Thomas G. Dietterich Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[64]  Cigdem Demir,et al.  Cost-conscious classifier ensembles , 2005, Pattern Recognit. Lett..

[65]  Cheng-Lin Liu,et al.  Classifier combination based on confidence transformation , 2005, Pattern Recognit..

[66]  Fuad Rahman,et al.  Multiple classifier decision combination strategies for character recognition: A review , 2003, Document Analysis and Recognition.

[67]  Christino Tamon,et al.  On the Boosting Pruning Problem , 2000, ECML.

[68]  Ethem Alpaydin,et al.  Ordering and finding the best of K > 2 supervised learning algorithms , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[69]  Michael J. Pazzani,et al.  A Principal Components Approach to Combining Regression Estimates , 1999, Machine Learning.

[70]  Rich Caruana,et al.  Ensemble selection from libraries of models , 2004, ICML.

[71]  João Gama,et al.  Local Cascade Generalization , 1998, International Conference on Machine Learning.

[72]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[73]  Robert P. W. Duin,et al.  Experiments with Classifier Combining Rules , 2000, Multiple Classifier Systems.

[74]  Ethem Alpaydın,et al.  Combined 5 x 2 cv F Test for Comparing Supervised Classification Learning Algorithms , 1999, Neural Comput..

[75]  Gonzalo Martínez-Muñoz,et al.  Using boosting to prune bagging ensembles , 2007, Pattern Recognit. Lett..