A Novel Locally and Globally Tuned Biogeography-based Optimization Algorithm

Giri, Parimal Kumar; De, Sagar S.; Dehuri, Satchidananda

doi:10.1007/978-981-10-5687-1_57

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Furthermore, in BBO the migration process is a probabilistic operator. This can update each habitat Hi by applying suitability index variables (SIVs) from higher HSI habitats [26]. The general migration model can be defined as in Eq.…”

Section: Biogeography-based Optimizationmentioning

confidence: 99%

A Locally and Globally Tuned Metaheuristic Optimization for Overlapping Community Detection

Mallick¹,

Giri²,

Mishra³

2023

MJCS

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Many people use online social networks to share their opinions and information in this digital age. The number of people engaged and their dynamic nature pose a major challenge for social network analysis (SNA). Community detection is one of the most critical and fascinating issues in social network analysis. Researchers frequently employ node features and topological structures to recognize important and meaningful performance in order to locate non-overlapping communities. We introduce a locally and globally tuned multi-objective biogeography-based optimization (LGMBBO) technique in this research for detecting overlapping communities based on the number of connections and node similarity. Four real- world online social network datasets were used in the experiment to assess the quality of both overlapping and non-overlapping partitions. As a result, the model generates a set of solutions that have the best topological structure of a network with node properties. The suggested model will increase their productivity and enhance their abilities to identify significant and pertinent communities.

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Section: Biogeography-based Optimizationmentioning

confidence: 99%

A Locally and Globally Tuned Metaheuristic Optimization for Overlapping Community Detection

Mallick¹,

Giri²,

Mishra³

2023

MJCS

View full text Add to dashboard Cite

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“…Furthermore, it has also no sufficient attributes for balancing between exploration and exploitation of the search space. To cope with these challenges, we have developed a variant of novel BBO algorithms, namely, locally and globally tuned BBO (LGBBO) (Giri et al, 2017a), Chaotic LGBBO (LGCBBO) (Giri et al, 2017b), and adaptive neighborhood for LGBBO (ANL-GBBO) (Giri et al, 2018). Therefore, by keeping in mind to improve the performance along with exploiting the accumulated search space and explore the large region to identify high-quality solutions, the locally and globally tuned BBO (LGBBO) algorithm (authors one of the improved BBO algorithms) has been inducted in this work for uncovering if-then classification rules from a credit scoring database.…”

Section: Related Workmentioning

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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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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An Experimental Study of Filter Bank Approach and Biogeography-Based Optimized ANN in Fingerprint Classification

Mishra¹,

Dehuri

2018

Nanoelectronics, Circuits and Communication Systems

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A Novel Locally and Globally Tuned Biogeography-based Optimization Algorithm

Cited by 6 publications

References 13 publications

A Locally and Globally Tuned Metaheuristic Optimization for Overlapping Community Detection

A Locally and Globally Tuned Metaheuristic Optimization for Overlapping Community Detection

Biogeography based optimization for mining rules to assess credit risk

An Experimental Study of Filter Bank Approach and Biogeography-Based Optimized ANN in Fingerprint Classification

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