Bibliographic Details
| Title: |
A hierarchical and parallel branch-and-bound ensemble selection algorithm. |
| Authors: |
Dai, Qun, Yao, ChangSheng |
| Source: |
Applied Intelligence; Jan2017, Vol. 46 Issue 1, p45-61, 17p |
| Subject Terms: |
Algorithms, Mathematical optimization, Machine learning, Soft computing, Accuracy |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |