Local decision bagging of binary neural classifiers

Authors:
Rocío Alaiz-Rodríguez
Affiliations:
Dpto. de Ingeniería Eléctrica y de Sistemas y Automática, Universidad de León, León, Spain
Venue:
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Year:
2008

Citing 15
Cited 0

The Strength of Weak Learnability

Machine Learning
Boosting a weak learning algorithm by majority

Information and Computation
Bagging predictors

Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates

IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting classifiers regionally

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Machine Learning
Ensembling neural networks: many could be better than all

Artificial Intelligence
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Local bagging of decision stumps

IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Minimax classifiers based on neural networks

Pattern Recognition
Selective ensemble of decision trees

RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Bagging tree classifiers for laser scanning images: a data- and simulation-based strategy

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Bagging as well as other classifier ensembles have made possible a performance improvement in many pattern recognition problems for the last decade. A careful analysis of previous work points out, however, that the most significant advance of bagged neural networks is achieved for multiclass problems, whereas the binary classification problems seldom benefit from the classifier combination. Focusing on the binary classification applications, this paper evaluates the standard bagging approach and explores a novel variant, local bagging, that, while keeping the standard individual classifier generation, attempts to improve its decision combination stage by (a) dynamically selecting of a set of individual classifiers and (b) subsequently weighting them by their local accuracy. Experimental results carried out on standard benchmark data sets with Neural Networks, SVMs, Naive Bayes, C4.5 Decision Trees and Decision Stumps as the base classifier, show that local bagging yields significant improvements in these practical applications and is found more stable than Adaboost.