Ensembling evidential k-nearest neighbor classifiers through multi-modal perturbation

Authors:
Hakan Altınçay
Affiliations:
Department of Computer Engineering, Eastern Mediterranean University, Mağusa, Northern Cyprus, Turkey
Venue:
Applied Soft Computing
Year:
2007

Citing 32
Cited 10

A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination

AI Magazine
On the Dempster-Shafer framework and new combination rules

Information Sciences: an International Journal
Implementing Dempster's rule for hierarchial evidence

Artificial Intelligence
A note on genetic algorithms for large-scale feature selection

Pattern Recognition Letters
The Combination of Evidence in the Transferable Belief Model

IEEE Transactions on Pattern Analysis and Machine Intelligence
The transferable belief model

Artificial Intelligence
Optimal combinations of pattern classifiers

Pattern Recognition Letters
Bagging predictors

Machine Learning
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multistage classifiers optimized by neural networks and genetic algorithms

Proceedings of the second world congress on Nonlinear analysts: part 3
The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive confidence transform based classifier combination for Chinese character recognition

Pattern Recognition Letters
An information theoretic framework for weight estimation in the combination of probabilistic classifiers for speaker identification

Speech Communication
Combining belief functions when evidence conflicts

Decision Support Systems
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms

Genetic Algorithms
Ensembling neural networks: many could be better than all

Artificial Intelligence
Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Nearest Neighbors in Random Subspaces

SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Comparison of Genetic Algorithm and Sequential Search Methods for Classifier Subset Selection

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Classification by evolutionary ensembles

Pattern Recognition
Multi-sensor fusion: an Evolutionary algorithm approach

Information Fusion
A new technique for combining multiple classifiers using the dempster-shafer theory of evidence

Journal of Artificial Intelligence Research
Constructing diverse classifier ensembles using artificial training examples

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Genetic algorithms in classifier fusion

Applied Soft Computing
Stopping criteria for ensemble of evolutionary artificial neural networks

Applied Soft Computing
An evidence-theoretic k-NN rule with parameter optimization

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Neural networks for classification: a survey

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Designing classifier fusion systems by genetic algorithms

IEEE Transactions on Evolutionary Computation
Cooperative coevolution of artificial neural network ensembles for pattern classification

IEEE Transactions on Evolutionary Computation
Ensembling local learners ThroughMultimodal perturbation

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Similarity computing model of high dimension data for symptom classification of Chinese traditional medicine

Applied Soft Computing
A feature selection technique for generation of classification committees and its application to categorization of laryngeal images

Pattern Recognition
Boosting k-nearest neighbor classifier by means of input space projection

Expert Systems with Applications: An International Journal
Selecting features from multiple feature sets for SVM committee-based screening of human larynx

Expert Systems with Applications: An International Journal
An information systems security risk assessment model under uncertain environment

Applied Soft Computing
MicroCBR: A case-based reasoning architecture for the classification of microarray data

Applied Soft Computing
Evidential cognitive maps

Knowledge-Based Systems
Random subspace evidence classifier

Neurocomputing
Evidential classifier for imprecise data based on belief functions

Knowledge-Based Systems
Classification of uncertain and imprecise data based on evidence theory

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Ensembling techniques have already been considered for improving the accuracy of k-nearest neighbor classifier. It is shown that using different feature subspaces for each member classifier, strong ensembles can be generated. Although it has a more flexible structure which is an obvious advantage from diversity point of view and is observed to provide better classification accuracies compared to voting based k-NN classifier, ensembling evidential k-NN classifier which is based on Dempster-Shafer theory of evidence is not yet fully studied. In this paper, we firstly investigate improving the performance of evidential k-NN classifier using random subspace method. Taking into account its potential to be perturbed also in parameter dimension due to its class and classifier dependent parameters, we propose ensembling evidential k-NN through multi-modal perturbation using genetic algorithms. Experimental results have shown that the improved accuracies obtained using random subspace method can be further surpassed through multi-modal perturbation.