Swarm intelligence
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Ant Colony Optimization
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A hybrid PSO/ACO algorithm for classification
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
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In this paper we propose a new method to improve the performance of hierarchical classification. We use a swarm intelligence algorithm to select the type of classification algorithm to be used at each "classifier node" in a classifier tree. These classifier nodes are used in a top-down divide and conquer fashion to classify the examples from hierarchical data sets. In this paper we propose a swarm intelligence based approach which attempts to mitigate a major drawback with a recently proposed local search-based, greedy algorithm. Our swarm intelligence based approach is able to take into account classifier interactions whereas the greedy algorithm is not. We evaluate our proposed method against the greedy method in four challenging bioinformatics data sets and find that, overall, there is a significant increase in performance.