Abstract. 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.
Holden and Freitas REF propose a method to improve the performance of top-down hierarchical classification, wherein a hybrid particle swarm optimisation (PSO) / ant colony optimisation (ACO) algorithm is used to select-out of a set of predefined candidate classification algorithms-the best (most accurate) classification algorithm to be used at each node of the class hierarchy.
Improving the Performance of Hierarchical Classification with Swarm Intelligence
EvoBIO
Computer Science