Dynamic integration of data mining methods in knowledge discovery systems

Esitetaan Jyvaskylan yliopiston informaatioteknologian tiedekunnan suostumuksella julkisesti tarkastettavaksi yliopiston Agora-rakennuksessa (Ag Aud. 2) joulukuun 19. paivana 2002 kello 12. Academic dissertation to be publicly discussed, by permission of the Faculty of Information Technology of the University of Jyvaskyla, in the Building Agora, (Ag Aud. 2), on December 19, 2002 at 12 o'clock noon.

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

[2]  Richard Bellman,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.

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

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

[5]  Padraig Cunningham,et al.  Diversity versus Quality in Classification Ensembles Based on Feature Selection , 2000, ECML.

[6]  Robert Tibshirani,et al.  Discriminant Adaptive Nearest Neighbor Classification , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Alexey Tsymbal,et al.  Dynamic integration of multiple data mining techniques in a knowledge discovery management system , 1999, Defense, Security, and Sensing.

[8]  Alexey Tsymbal,et al.  Ensemble Feature Selection with Dynamic Integration of Classifiers , 2001 .

[9]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[10]  Huan Liu,et al.  Feature Selection for Classification , 1997, Intell. Data Anal..

[11]  Michael J. Pazzani,et al.  Classification and regression by combining models , 1998 .

[12]  Salvatore J. Stolfo,et al.  An extensible meta-learning approach for scalable and accurate inductive learning , 1996 .

[13]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[14]  Mark A. Hall,et al.  Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning , 1999, ICML.

[15]  Alexey Tsymbal,et al.  Learning feature selection for medical databases , 1999, Proceedings 12th IEEE Symposium on Computer-Based Medical Systems (Cat. No.99CB36365).

[16]  Saso Dzeroski,et al.  Combining Multiple Models with Meta Decision Trees , 2000, PKDD.

[17]  Ron Kohavi,et al.  Wrappers for performance enhancement and oblivious decision graphs , 1995 .

[18]  Pedro M. Domingos.  Control-Sensitive Feature Selection for Lazy Learners , 1997, Artificial Intelligence Review.

[19]  Christopher J. Merz,et al.  UCI Repository of Machine Learning Databases , 1996 .

[20]  Marvin Minsky,et al.  Perceptrons: An Introduction to Computational Geometry , 1969 .

[21]  Ron Kohavi,et al.  Data Mining Using MLC a Machine Learning Library in C++ , 1996, Int. J. Artif. Intell. Tools.

[22]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[23]  Moshe Koppel Sean P. Engelson.  Integrating Multiple Classifiers By Finding Their Areas of Expertise , 1996 .

[24]  Alexey Tsymbal,et al.  Advanced dynamic selection of diagnostic methods , 1998, Proceedings. 11th IEEE Symposium on Computer-Based Medical Systems (Cat. No.98CB36237).

[25]  Charles Elkan,et al.  Boosting and Naive Bayesian learning , 1997 .

[26]  Michael J. Pazzani,et al.  Error reduction through learning multiple descriptions , 2004, Machine Learning.

[27]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[28]  Robert E. Schapire,et al.  A Brief Introduction to Boosting , 1999, IJCAI.

[29]  Cullen Schaffer,et al.  Selecting a classification method by cross-validation , 1993, Machine Learning.

[30]  Kagan Tumer,et al.  Classifier Combining: Analytical Results and Implications , 1995 .

[31]  Anders Krogh,et al.  Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.

[32]  Se June Hong,et al.  Use of Contextaul Information for Feature Ranking and Discretization , 1997, IEEE Trans. Knowl. Data Eng..

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

[34]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[35]  Thomas G. Dietterich,et al.  A study of distance-based machine learning algorithms , 1994 .

[36]  Wei-Yin Loh,et al.  A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms , 2000, Machine Learning.

[37]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[38]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

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

[40]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[41]  Alexey Tsymbal,et al.  Arbiter Meta-Learning with Dynamic Selection of Classifiers and Its Experimental Investigation , 1999, ADBIS.

[42]  Peter Kokol,et al.  Comparison of Three Databases with a Decision Tree Approach in the Medical Field of Acute Appendicitis , 2001, MedInfo.

[43]  David W. Opitz,et al.  Feature Selection for Ensembles , 1999, AAAI/IAAI.

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

[45]  Edwin P. D. Pednault,et al.  Decomposition of Heterogeneous Classification Problems , 1997, IDA.

[46]  Pedro M. Domingos,et al.  Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier , 1996, ICML.

[47]  Sebastian Thrun,et al.  The MONK''s Problems-A Performance Comparison of Different Learning Algorithms, CMU-CS-91-197, Sch , 1991 .

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

[49]  Alexey Tsymbal,et al.  Distance functions in dynamic integration of data mining techniques , 2000, SPIE Defense + Commercial Sensing.

[50]  V. Terziyan,et al.  Dynamic Integration of Data Mining Methods Using Selection in a Knowledge Discovery Management System , 1999 .

[51]  Richard Maclin,et al.  Ensembles as a Sequence of Classifiers , 1997, IJCAI.

[52]  Alexey Tsymbal,et al.  Decision Committee Learning with Dynamic Integration of Classifiers , 2000, ADBIS-DASFAA.

[53]  Robert E. Schapire,et al.  Using output codes to boost multiclass learning problems , 1997, ICML.

[54]  Ron Kohavi,et al.  MineSet: An Integrated System for Data Mining , 1997, KDD.

[55]  Mario Vento,et al.  Reliability Parameters to Improve Combination Strategies in Multi-Expert Systems , 1999, Pattern Analysis & Applications.

[56]  Alexey Tsymbal,et al.  A Dynamic Integration Algorithm for an Ensemble of Classifiers , 1999, ISMIS.

[57]  João Gama,et al.  Combining classification algorithms , 2000 .

[58]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[59]  Ron Kohavi,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998 .

[60]  Alexey Tsymbal,et al.  Ensemble feature selection with the simple Bayesian classification , 2003, Inf. Fusion.

[61]  Claire Cardie,et al.  Improving Minority Class Prediction Using Case-Specific Feature Weights , 1997, ICML.

[62]  Heikki Mannila,et al.  A database perspective on knowledge discovery , 1996, CACM.

[63]  Alexey Tsymbal,et al.  Local Feature Selection with Dynamic Integration of Classifiers , 2001, Fundam. Informaticae.

[64]  Peter W. Eklund.  Comparative study of public-domain supervised machine-learning accuracy on the UCI database , 1999, Defense, Security, and Sensing.

[65]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[66]  Alexander Schnabl,et al.  Development of Multi-Criteria Metrics for Evaluation of Data Mining Algorithms , 1997, KDD.

[67]  David W. Opitz,et al.  Generating Accurate and Diverse Members of a Neural-Network Ensemble , 1995, NIPS.

[68]  Salvatore J. Stolfo,et al.  On the Accuracy of Meta-learning for Scalable Data Mining , 2004, Journal of Intelligent Information Systems.

[69]  Padraig Cunningham,et al.  Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error , 2001, ECML.

[70]  Tony R. Martinez,et al.  Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..

[71]  B. Gorayska,et al.  Cognitive Technology: In Search of a Humane Interface , 1995 .

[72]  Wolfgang Gaul,et al.  Classification and Positioning of Data Mining Tools , 1999 .

[73]  William G. Baxt,et al.  Improving the Accuracy of an Artificial Neural Network Using Multiple Differently Trained Networks , 1992, Neural Computation.

[74]  Padhraic Smyth,et al.  Knowledge Discovery and Data Mining: Towards a Unifying Framework , 1996, KDD.

[75]  Ted Pedersen,et al.  A Simple Approach to Building Ensembles of Naive Bayesian Classifiers for Word Sense Disambiguation , 2000, ANLP.

[76]  Kagan Tumer,et al.  Error Correlation and Error Reduction in Ensemble Classifiers , 1996, Connect. Sci..

[77]  Christopher J. Merz,et al.  Dynamical Selection of Learning Algorithms , 1995, AISTATS.

[78]  Steven L. Salzberg.  On Comparing Classifiers: A Critique of Current Research and Methods , 1999 .

[79]  David A. Bell,et al.  Designing a Kernel for Data Mining , 1997, IEEE Expert.

[80]  George H. John.  Enhancements to the data mining process , 1997 .

[81]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[82]  Alexey Tsymbal,et al.  The decision support system for telemedicine based on multiple expertise , 1998, Int. J. Medical Informatics.

[83]  Kevin W. Bowyer,et al.  Combination of multiple classifiers using local accuracy estimates , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[84]  Kagan Tumer,et al.  Dimensionality Reduction Through Classifier Ensembles , 1999 .

[85]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[86]  Pedro M. Domingos.  Knowledge Discovery Via Multiple Models , 1998, Intell. Data Anal..

[87]  Geoffrey I. Webb,et al.  MultiBoosting: A Technique for Combining Boosting and Wagging , 2000, Machine Learning.

[88]  Fabio Roli,et al.  Methods for dynamic classifier selection , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[89]  Steven Salzberg,et al.  A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features , 2004, Machine Learning.

[90]  Alexey Tsymbal,et al.  Ensemble feature selection with the simple Bayesian classification in medical diagnostics , 2002, Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002).

[91]  J. Ross Quinlan,et al.  Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.

[92]  Alexey Tsymbal,et al.  Bagging and Boosting with Dynamic Integration of Classifiers , 2000, PKDD.

[93]  Thomas G. Dietterich.  Machine-Learning Research Four Current Directions , 1997 .

[94]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[95]  Fabio Roli,et al.  Dynamic classifier selection based on multiple classifier behaviour , 2001, Pattern Recognit..