title: Novel layered clustering-based approach for generating ensemble of classifiers.
abstract: 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.
authors.affiliation: Central Queensland University, Rockhampton, Australia. a.rahman@cqu.edu.au
authors.forename: Ashfaqur
authors.initials: A
authors.lastname: Rahman
authors.forename: Brijesh
authors.initials: B
authors.lastname: Verma
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D006801:Humans; D008432:Mathematical Computing; D055641:Mathematical Concepts; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000592:standards*; D012986:Software Validation; D013223:Statistics as Topic / Q000379:methods