Abstract
Ensemble pruning has been widely applied to improve the capacity of multiple learner system. Both diversity and classification accuracy of learners are considered as two key factors for achieving an ensemble with competitive classification ability. Considering that extreme learning machine (ELM) is characterized by excellent training rate and generalization capability, it is employed as the base classifier. For a multiple ELM system, when we increase its constituents’ diversity, the mean accuracy of the whole members must be decreased. Therefore, a compromise between them can ensure that the ELMs remain good diversity and high precision, but finding the compromise brings a heavy computational burden. It is hard to look for the exact result via the searching of intelligent algorithms or pruning of diversity measures. On the basis, we propose a hybrid ensemble pruning approach employing coevolution binary glowworm swarm optimization and reduce-error (HEPCBR). Considering the good performance of reduce-error (RE) in selecting ELMs with high diversity and precision, we try to employ RE to choose the satisfactory ELMs from the generated ELMs. In addition, the constituents are further selected via the proposed coevolution binary glowworm swarm optimization, which are utilized to construct the promising ensemble. Experimental results indicate that, compared to other frequently used methods, the proposed HEPCBR achieves significantly superior performance in classification.
Highlights
Ensemble learning is widely used to improve classification ability in the areas such as image recognition [1, 2], intelligent detection [3, 4], and data mining [5, 6]
Is work’s contributions have been presented as follows: (1) An ensemble pruning algorithm, named HEPCBR, which utilizes the integration of RE and the proposed CBGSO
The extreme learning machine (ELM) with poor comprehensive performance are prepruned using RE for downsizing ELMs and notably reducing the computational overheads of the selection of ELMs; second, the subensemble of ELMs are chosen from the remaining ELMs using the proposed CBGSO. e proposed HEPCBR is described as follows
Summary
Ensemble learning is widely used to improve classification ability in the areas such as image recognition [1, 2], intelligent detection [3, 4], and data mining [5, 6]. HEPCBR is an integrated method for dealing with ensemble pruning of multiple ELMs, which performs well in terms of searching for the optimal combination of ELMs. We remove some redundant ELMs using RE for reducing the size of ELMs and alleviating the computational complexity, and the presented CBGSO is adopted to further seek for the optimal subensemble of ELMs. erefore, the combination of CBGSO and RE can cope with the selection of ELMs. is work’s contributions have been presented as follows:. (1) An ensemble pruning algorithm, named HEPCBR, which utilizes the integration of RE and the proposed CBGSO (2) We modify the basic GSO, named CBGSO, which has good convergence accuracy and high evolution velocity (3) RE takes full advantage of the diversity among ELMs and filters out a fraction of ELMs with poor comprehensive performance (4) Experimental results show the proposed approach can obtain a significant enhancement in classification capacity. If the glowworm wins the same with the locally optimal solution, several random elements of the glowworm will be changed, so as to move forward other position for escaping from the local optima
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