A new ensemble method for gold mining problems: Predicting technology transfer
Electronic Commerce Research and Applications
Predicting shellfish farm closures with class balancing methods
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Ensemble classifier generation using non-uniform layered clustering and Genetic Algorithm
Knowledge-Based Systems
Ensemble Feature Ranking for Shellfish Farm Closure Cause Identification
Proceedings of Workshop on Machine Learning for Sensory Data Analysis
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This paper introduces a novel concept for creating an ensemble of classifiers. The concept is based on generating an ensemble of classifiers through clustering of data at multiple layers. The ensemble classifier model generates a set of alternative clustering of a dataset at different layers by randomly initializing the clustering parameters and trains a set of base classifiers on the patterns at different clusters in different layers. A test pattern is classified by first finding the appropriate cluster at each layer and then using the corresponding base classifier. The decisions obtained at different layers are fused into a final verdict using majority voting. As the base classifiers are trained on overlapping patterns at different layers, the proposed approach achieves diversity among the individual classifiers. Identification of difficult-to-classify patterns through clustering as well as achievement of diversity through layering leads to better classification results as evidenced from the experimental results.