Improved Biogeography-Based Optimization Algorithm and Its Application to Clustering Optimization and Medical Image Segmentation

Zhang, Xinming; Wang, Doudou; Chen, Haiyan

doi:10.1109/access.2019.2901849

Cited by 31 publications

(18 citation statements)

References 39 publications

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“…From this table, we have observed that meta-heuristic based rule miners are very competitive to the hybride metaheuristic based classification on credit score assessment and can add another dimension to the field of rule mining with a great success of classifying an unseen sample if work on it can be intensified. As a consequence, several credit scoring models make use of hybrid mining approaches to support credit approval decisions (Zhang, Wang, & Chen, 2019). Initially, parametric statistical techniques have employed for credit scoring.…”

Section: Related Workmentioning

confidence: 99%

“…In general, the selection operator is used to assess the probability of As a result, it is very often noticed that a bad model is directly influenced by a rule which is better in a local context rather than a global context. Moreover, migration and mutation operators are two imperative features that largely affect the performance and computational efficiency in BBO, which maintain both exploration and exploitation of existing approaches (Zhang et al, 2019).…”

Section: Selectionmentioning

confidence: 99%

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Biogeography based optimization for mining rules to assess credit risk

Giri

Dehuri

et al. 2021

Intell Sys Acc Fin Mgmt

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Financial institutions, by and large, rely on the use of machine learning techniques to improve the classic credit risk assessment model for reduction of costs, delivery of faster decisions, guaranteed credit collections, and risk mitigations. As such, several data mining and machine learning approaches have been developed for computation of credit scores over the last few decades. Moreover, the existing rule-based classification algorithms tend to generate a number of rules with a large number of conditions in the antecedent part. However, these algorithms fail to demonstrate high predictive accuracy while balancing coverage and simplicity. Thus, it becomes quite a challenging task for the researchers to generate an optimal rule set with high predictive accuracy. In this paper, we present an effective rule based classification technique for the prediction of credit risk using a novel Biogeography Based Optimization (BBO) method. The novel BBO in the context of rule mining is named as locally and globally tuned biogeography based rule-miner (LGBBO-RuleMiner). This is applied for discovering optimal rule set with high predictive accuracy from the dataset containing both the categorical and continuous attributes. The performance of the proposed algorithm is compared against a variety of rule-miners such as OneR (1R), PART, JRip, Decision Table, Conjunctive Rule, J48, and Random Tree, along with some meta-heuristic based rule mining techniques by considering two credit risk datasets obtained from University of California, Irvine (UCI) repository. It is found from the comparative study that the proposed rule miner in ten independent runs of tenfold cross validation outperforms all of the aforesaid algorithms in terms of predictive accuracy, coverage, and simplicity.

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Section: Related Workmentioning

confidence: 99%

Section: Selectionmentioning

confidence: 99%

Biogeography based optimization for mining rules to assess credit risk

Giri

Dehuri

et al. 2021

Intell Sys Acc Fin Mgmt

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“…In addition, the following improvements are also adopted. First, the greedy selection [24,37] replaces the elitism strategy [8]. So, on the one hand, the population is just needed to sort once at each iteration to reduce computing complex; on the other hand, the greedy selection avoids setting the elitist parameter.…”

Section: Dynamic Two-differential Perturbing Operator De Proposed Bymentioning

confidence: 99%

“…GLxBBO is an incomplete variant of ILxBBO, which is LxBBO with only the two-global-best guiding operator, without the dynamic two-differential perturbing operator and improved Laplace migration operator DLxBBO is an incomplete variant of ILxBBO, which is LxBBO with only the dynamic two-differential perturbing operator without the improved Laplace migration operator and two-global-best guiding operator OLxBBO is an incomplete variant of ILxBBO, which contains the improved Laplace migration operator without the dynamic two-differential perturbing operator and two-global-best guiding operator e experimental results are shown in Table 1 [39], BIBBO [25], BBOM [40], DEBBO [30], BLPSO [41], PRBBO [24], WRBBO [37], EMBBO [27], and BHCS [42]. ese algorithms are all BBO variants proposed in recent years, with much comparability.…”

Section: Comparison Of Ilxbbo With Its Incomplete Variantsmentioning

confidence: 99%

Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

et al. 2020

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Laplacian Biogeography-Based Optimization (LxBBO) is a BBO variant which improves BBO’s performance largely. When it solves some complex problems, however, it has some drawbacks such as poor performance, weak operability, and high complexity, so an improved LxBBO (ILxBBO) is proposed. First, a two-global-best guiding operator is created for guiding the worst habitat mainly to enhance the exploitation of LxBBO. Second, a dynamic two-differential perturbing operator is proposed for the first two best habitats’ updating to improve the global search ability in the early search phase and the local one in the late search one, respectively. Third, an improved Laplace migration operator is formulated for other habitats’ updating to improve the search ability and the operability. Finally, some measures such as example learning, mutation operation removing, and greedy selection are adopted mostly to reduce the computation complexity of LxBBO. A lot of experimental results on the complex functions from the CEC-2013 test set show ILxBBO obtains better performance than LxBBO and quite a few state-of-the-art algorithms do. Also, the results on Quadratic Assignment Problems (QAPs) show that ILxBBO is more competitive compared with LxBBO, Improved Particle Swarm Optimization (IPSO), and Improved Firefly Algorithm (IFA).

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“…Species living in a geography that has high HSI emigrates to nearby habitats, which has low species, since this biogeography is already nearly saturated. BBO has been used for clustering in some studies [22,23]. As seen in Table 3, since BBO algorithm uses three loops, BBO runs slower than the other algorithms like PSO and GWO.…”

Section: Biogeography-based Optimizationmentioning

confidence: 99%