The Strength of Weak Learnability
Machine Learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
An analysis of diversity measures
Machine Learning
Rapid and brief communication: FuzzyBagging: A novel ensemble of classifiers
Pattern Recognition
Initialization Dependence of Clustering Algorithms
Advances in Neuro-Information Processing
The Diversity of Regression Ensembles Combining Bagging and Random Subspace Method
Advances in Neuro-Information Processing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
IEEE Transactions on Neural Networks
Ensembling Heterogeneous Learning Models with Boosting
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ensemble classifier generation using non-uniform layered clustering and Genetic Algorithm
Knowledge-Based Systems
Effect of ensemble classifier composition on offline cursive character recognition
Information Processing and Management: an International Journal
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In this paper we present an approach to generate ensemble of classifiers using non-uniform layered clustering. In the proposed approach the dataset is partitioned into variable number of clusters at different layers. A set of base classifiers is trained on the clusters at different layers. The decision on a pattern at each layer is obtained from the classifier trained on the nearest cluster and the decisions from the different layers are fused using majority voting to obtain the final verdict. The proposed approach provides a mechanism to obtain the optimal number of layers and clusters using a Genetic Algorithm. Clustering identifies difficult-to-classify patterns and layered non-uniform clustering approach brings in diversity among the base classifiers at different layers. The proposed method performs relatively better than the other state-of-art ensemble classifier generation methods as evidenced from the experimental results.