Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院
Journal:APPLIED INTELLIGENCE
Key Words:Ensemble selection Multiple Classifier Systems (MCSs) Hierarchical and Parallel Branch-and-Bound Ensemble Selection (H&PB&BEnS) algorithm Branch-and-Bound (B&B) algorithm
Abstract:This paper describes the development of an effective and efficient Hierarchical and Parallel Branch-and-Bound Ensemble Selection (H&PB&BEnS) algorithm. Using the proposed H&PB&BEnS, ensemble selection is accomplished in a divisional, parallel, and hierarchical way. H&PB&BEnS uses the superior performance of the Branch-and-Bound (B&B) algorithm in relation to small-scale combinational optimization problems, whilst also managing to avoid "the curse of dimensionality" that can result from the direct application of B&B to ensemble selection problems. The B&B algorithm is used to select each partitioned subensemble, which enhances the predictive accuracy of each pruned subsolution, and then the working mechanism of H&PB&BEnS improves the diversity of the ensemble selection results. H&PB&BEnS realizes layer-wise refinement of the selected ensemble solutions, which enables the classification performance of the selected ensembles to be improved in a layer-by-layer manner. Empirical investigations are conducted using five benchmark classification datasets, and the results verify the effectiveness and efficiency of the proposed H&PB&BEnS algorithm.
ISSN No.:0924-669X
Translation or Not:no
Date of Publication:2017-01-01
Co-author:姚长生
Correspondence Author:dq
Date of Publication:2017-01-01