Bi-clustering by Multi-objective Evolutionary Algorithm for Multimodal Analytics and Big Data

Golchin, Maryam; Liew, Alan Wee-Chung

doi:10.1007/978-3-319-97598-6_6

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2020

2024

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Cited by 2 publications

(1 citation statement)

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“…Golchin and Wee-Chung Liew, [26] first represent standard bi-bunch concepts that are commonly used. Then it is depicted a few groundbreaking bi-group formulas focused on their target capacities, search mechanisms and whether they support their performance.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review of Multi-objective Evolutionary Algorithms for Information Retrieval System

2020

IJETER

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Information retrieval systems (IRSs) presentation is generally calculated using two special principles, accuracy and analysis. Exactness is the proportion of appropriate documents retrieved by the Information Retrieval System to the overall number of archives retrieved in the context of a client's inquiry; while review is the proportion of the quantity of pertinent reports recovered to the complete number of important reports for the client's question that are present in the narrative database. We present an investigation of two notable broadly useful multi-objective transformative algorithms, Challenges of Multi-objective Evolutionary Algorithms, Applications and Recent improvements in Multi-objective Evolutionary Algorithms all briefly. Further this examination talks about the diverse writing attempts to make the examination.

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Section: Literature Reviewmentioning

confidence: 99%

A Review of Multi-objective Evolutionary Algorithms for Information Retrieval System

2020

IJETER

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Scaling Multiobjective Evolution to Large Data With Minions: A Bayes-Informed Multitask Approach

Chen

Gupta

Zhou

et al. 2024

IEEE Trans. Cybern.

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Bi-clustering by Multi-objective Evolutionary Algorithm for Multimodal Analytics and Big Data

Cited by 2 publications

References 46 publications

A Review of Multi-objective Evolutionary Algorithms for Information Retrieval System

A Review of Multi-objective Evolutionary Algorithms for Information Retrieval System

Scaling Multiobjective Evolution to Large Data With Minions: A Bayes-Informed Multitask Approach

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