Hybrid ensemble selection algorithm incorporating GRASP with path relinking

The greedy randomized adaptive search procedure (GRASP) is an iterative two-phase multi-start metaheuristic procedure for a combination optimization problem, while path relinking is an intensification procedure applied to the solutions generated by GRASP. In this paper, a hybrid ensemble selection a...

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Vydané v:	Applied intelligence (Dordrecht, Netherlands) Ročník 44; číslo 3; s. 704 - 724
Hlavní autori:	Zhang, Ting, Dai, Qun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.04.2016 Springer Nature B.V
Predmet:	Accuracy Algorithms Artificial Intelligence Computer Science Convergence Credit risk Credit scoring Datasets Discriminant analysis Financial institutions Learning Linear programming Machines Manufacturing Mechanical Engineering Multilayers Neural networks Optimization Processes Scoring Searching Support vector machines Teaching methods Extreme learning machine (ELM) Ensemble selection algorithm incorporating GRASP with path relinking (PRelinkGraspEnS) Path relinking Greedy randomized adaptive search procedure (GRASP) Bootstrap aggregation (bagging)
ISSN:	0924-669X, 1573-7497
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Abstract	The greedy randomized adaptive search procedure (GRASP) is an iterative two-phase multi-start metaheuristic procedure for a combination optimization problem, while path relinking is an intensification procedure applied to the solutions generated by GRASP. In this paper, a hybrid ensemble selection algorithm incorporating GRASP with path relinking (PRelinkGraspEnS) is proposed for credit scoring. The base learner of the proposed method is an extreme learning machine (ELM). Bootstrap aggregation (bagging) is used to produce multiple diversified ELMs, while GRASP with path relinking is the approach for ensemble selection. The advantages of the ELM are inherited by the new algorithm, including fast learning speed, good generalization performance, and easy implementation. The PRelinkGraspEnS algorithm is able to escape from local optima and realizes a multi-start search. By incorporating path relinking into GRASP and using it as the ensemble selection method for the PRelinkGraspEnS the proposed algorithm becomes a procedure with a memory and high convergence speed. Three credit datasets are used to verify the efficiency of our proposed PRelinkGraspEnS algorithm. Experimental results demonstrate that PRelinkGraspEnS achieves significantly better generalization performance than the classical directed hill climbing ensemble pruning algorithm, support vector machines, multi-layer perceptrons, and a baseline method, the best single model. The experimental results further illustrate that by decreasing the average time needed to find a good-quality subensemble for the credit scoring problem, GRASP with path relinking outperforms pure GRASP (i.e., without path relinking).
AbstractList	The greedy randomized adaptive search procedure (GRASP) is an iterative two-phase multi-start metaheuristic procedure for a combination optimization problem, while path relinking is an intensification procedure applied to the solutions generated by GRASP. In this paper, a hybrid ensemble selection algorithm incorporating GRASP with path relinking (PRelinkGraspEnS) is proposed for credit scoring. The base learner of the proposed method is an extreme learning machine (ELM). Bootstrap aggregation (bagging) is used to produce multiple diversified ELMs, while GRASP with path relinking is the approach for ensemble selection. The advantages of the ELM are inherited by the new algorithm, including fast learning speed, good generalization performance, and easy implementation. The PRelinkGraspEnS algorithm is able to escape from local optima and realizes a multi-start search. By incorporating path relinking into GRASP and using it as the ensemble selection method for the PRelinkGraspEnS the proposed algorithm becomes a procedure with a memory and high convergence speed. Three credit datasets are used to verify the efficiency of our proposed PRelinkGraspEnS algorithm. Experimental results demonstrate that PRelinkGraspEnS achieves significantly better generalization performance than the classical directed hill climbing ensemble pruning algorithm, support vector machines, multi-layer perceptrons, and a baseline method, the best single model. The experimental results further illustrate that by decreasing the average time needed to find a good-quality subensemble for the credit scoring problem, GRASP with path relinking outperforms pure GRASP (i.e., without path relinking).
Author	Zhang, Ting Dai, Qun
Author_xml	– sequence: 1 givenname: Ting surname: Zhang fullname: Zhang, Ting organization: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics – sequence: 2 givenname: Qun surname: Dai fullname: Dai, Qun email: daiqun@nuaa.edu.cn organization: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics
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Keywords	Extreme learning machine (ELM) Ensemble selection algorithm incorporating GRASP with path relinking (PRelinkGraspEnS) Path relinking Greedy randomized adaptive search procedure (GRASP) Bootstrap aggregation (bagging)
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Snippet	The greedy randomized adaptive search procedure (GRASP) is an iterative two-phase multi-start metaheuristic procedure for a combination optimization problem,...
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SubjectTerms	Accuracy Algorithms Artificial Intelligence Computer Science Convergence Credit risk Credit scoring Datasets Discriminant analysis Financial institutions Learning Linear programming Machines Manufacturing Mechanical Engineering Multilayers Neural networks Optimization Processes Scoring Searching Support vector machines Teaching methods
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Title	Hybrid ensemble selection algorithm incorporating GRASP with path relinking
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