title: Evolved feature weighting for random subspace classifier.
abstract: The problem addressed in this letter concerns the multiclassifier generation by a random subspace method (RSM). In the RSM, the classifiers are constructed in random subspaces of the data feature space. In this letter, we propose an evolved feature weighting approach: in each subspace, the features are multiplied by a weight factor for minimizing the error rate in the training set. An efficient method based on particle swarm optimization (PSO) is here proposed for finding a set of weights for each feature in each subspace. The performance improvement with respect to the state-of-the-art approaches is validated through experiments with several benchmark data sets.
authors.affiliation: DEIS, IEIIT- Universita di Bologna, 40136 Bologna, Italy. loris.nanni@unibo.it
authors.forename: L
authors.initials: L
authors.lastname: Nanni
authors.forename: A
authors.initials: A
authors.lastname: Lumini
mesh_terms: D001185:Artificial Intelligence; D016000:Cluster Analysis; D016247:Information Storage and Retrieval; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results