A hierarchical and parallel branch-and-bound ensemble selection algorithm

作者：Dai, Qun^*; Yao, ChangSheng

来源：Applied Intelligence, 2017, 46(1): 45-61.

DOI：10.1007/s10489-016-0817-8

• Ensemble selection
• Multiple Classifier Systems (MCSs)
• Hierarchical and Parallel Branch-and-Bound Ensemble Selection (H&PB&BEnS) algorithm
• Branch-and-Bound (B&B) algorithm

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.

• 出版日期2017-1
• 单位南京航空航天大学