Neural networks and the bias/variance dilemma
Neural Computation
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments with Classifier Combining Rules
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Support Vector Data Description
Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using diversity of errors for selecting members of a committee classifier
Pattern Recognition
Adaptive fusion and co-operative training for classifier ensembles
Pattern Recognition
Optimizing number of hidden neurons in neural networks
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Advances of Research in Fuzzy Integral for Classifiers' fusion
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02
Engineering multiversion neural-net systems
Neural Computation
Simplifying Particle Swarm Optimization
Applied Soft Computing
Data dependence in combining classifiers
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Comparison of classifier selection methods for improving committee performance
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Tuning SVM parameters by using a hybrid CLPSO-BFGS algorithm
Neurocomputing
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, a new method is introduced to construct Multiple Classifier Systems (MCSs). It is based on controlling diversity among base classifiers according to a new method in measuring diversity in kernel space. The method admits a tradeoff between individual classifier and multiple classifier accuracy and diversity as each base classifier requires knowledge of the choices made by the other MCS members. This knowledge is included in the method using data descriptors as a tool for creating diversity between base classifiers in kernel space. Data description properties are also used for measuring diversity. A new combining method presented in this paper completes this work. Performance of the proposed method is evaluated on a number of known benchmark datasets. Analyzing the results shows that the proposed method improves system's overall performance and accuracy in many cases. It also measures diversity more precisely.