An Empirical Comparison of Voting Classification Algorithms

Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and real-world data...

[1]  Corinna Cortes,et al.  Boosting Decision Trees , 1995, NIPS.

[2]  Bojan Cestnik,et al.  Estimating Probabilities: A Crucial Task in Machine Learning , 1990, ECAI.

[3]  David H. Wolpert,et al.  The Relationship Between PAC, the Statistical Physics Framework, the Bayesian Framework, and the VC Framework , 1995 .

[4]  Cullen Schaffer,et al.  A Conservation Law for Generalization Performance , 1994, ICML.

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

[6]  Michael J. Pazzani,et al.  Reducing Misclassification Costs , 1994, ICML.

[7]  Jude W. Shavlik,et al.  Learning Symbolic Rules Using Artificial Neural Networks , 1993, ICML.

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

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

[10]  Pat Langley,et al.  An Analysis of Bayesian Classifiers , 1992, AAAI.

[11]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[12]  Pedro M. Domingos.  Why Does Bagging Work? A Bayesian Account and its Implications , 1997, KDD.

[13]  M. Pazzani,et al.  Learning probabilistic relational concept descriptions , 1996 .

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

[15]  J. R. Quinlan,et al.  Comparing connectionist and symbolic learning methods , 1994, COLT 1994.

[16]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[17]  Wray L. Buntine,et al.  Learning classification trees , 1992 .

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

[19]  Yoav Freund,et al.  Boosting a weak learning algorithm by majority , 1995, COLT '90.

[20]  Thomas G. Dietterich,et al.  Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs , 1991, AAAI.

[21]  Ron Kohavi,et al.  Bias Plus Variance Decomposition for Zero-One Loss Functions , 1996, ICML.

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

[23]  Thomas G. Dietterich,et al.  Error-Correcting Output Coding Corrects Bias and Variance , 1995, ICML.

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

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

[26]  Chris Carter,et al.  Multiple decision trees , 2013, UAI.

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

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

[29]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[30]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[31]  Tim Oates,et al.  The Effects of Training Set Size on Decision Tree Complexity , 1997, ICML.