Creating ensembles of classifiers via fuzzy clustering and deflection

Authors:
Huaxiang Zhang;Jing Lu
Affiliations:
Department of Computer Science, Shandong Normal University, Jinan 250014, Shandong, China;Department of Computer Science, Shandong College of Finance, Jinan 250014, Shandong, China
Venue:
Fuzzy Sets and Systems
Year:
2010

Citing 23
Cited 3

Bagging predictors

Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates

IEEE Transactions on Pattern Analysis and Machine Intelligence
MultiBoosting: A Technique for Combining Boosting and Wagging

Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy

Machine Learning
Combining Decision Trees and Neural Networks for Drug Discovery

EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Rotation Forest: A New Classifier Ensemble Method

IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification by evolutionary ensembles

Pattern Recognition
Rapid and brief communication: FuzzyBagging: A novel ensemble of classifiers

Pattern Recognition
Ensemblator: An ensemble of classifiers for reliable classification of biological data

Pattern Recognition Letters
Nonlinear Boosting Projections for Ensemble Construction

The Journal of Machine Learning Research
A local boosting algorithm for solving classification problems

Computational Statistics & Data Analysis
Genetic algorithm-based feature set partitioning for classification problems

Pattern Recognition
RotBoost: A technique for combining Rotation Forest and AdaBoost

Pattern Recognition Letters
Ensemble generation and feature selection for the identification of students with learning disabilities

Expert Systems with Applications: An International Journal
Optimal ensemble construction via meta-evolutionary ensembles

Expert Systems with Applications: An International Journal
Letters: Multihashing, human authentication featuring biometrics data and tokenized random number: A case study FVC2004

Neurocomputing
Letters: Ensemble of classifiers for protein fold recognition

Neurocomputing
Switching class labels to generate classification ensembles

Pattern Recognition
Boosting with averaged weight vectors

MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Using all data to generate decision tree ensembles

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Cooperative coevolution of artificial neural network ensembles for pattern classification

IEEE Transactions on Evolutionary Computation

Supporting system for detecting pathologies

IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Making Diversity Enhancement Based on Multiple Classifier System by Weight Tuning

Neural Processing Letters
A multi-agent system for web-based risk management in small and medium business

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.21

Visualization

Abstract

Ensembles of classifiers can increase the performance of pattern recognition, and have become a hot research topic. High classification accuracy and diversity of the component classifiers are essential to obtain good generalization capability of an ensemble. We review the methods used to learn diverse classifiers, employ fuzzy clustering with deflection to learn the distribution characteristics of the training data, and propose a novel sampling approach to generate training data sets for the component classifiers. Our approach increases the classification accuracy and diversity of the component classifiers. The approach is evaluated using the base classifier c4.5, and the experimental results show that it outperforms Bagging and AdaBoost on almost all the randomly selected 20 benchmark UCI data sets.