An Evolutionary Multitasking Method for High-Dimensional Receiver Operating Characteristic Convex Hull Maximization

Maximizing receiver operating characteristic convex hull (ROCCH) is a hot research topic of binary classification, since it can obtain good classifiers under either balanced or imbalanced situation. Recently, evolutionary algorithms (EAs) especially multi-objective evolutionary algorithms (MOEAs) ha...

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Published in:IEEE transactions on emerging topics in computational intelligence 2024-04, Vol.8 (2), p.1699-1713
Main Authors: Cheng, Fan, Shu, Shengda, Zhang, Lei, Tan, Ming, Qiu, Jianfeng
Format: Article
Language:English
Subjects:
classification
Classification algorithms
Convexity
Evolutionary algorithms
Evolutionary computation
Evolutionary multitasking
Genetic algorithms
Knowledge management
Knowledge transfer
Maximization
Measurement
multi-objective evolutionary algorithm
Multitasking
Optimization
Receivers
ROC convex hull
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Summary:Maximizing receiver operating characteristic convex hull (ROCCH) is a hot research topic of binary classification, since it can obtain good classifiers under either balanced or imbalanced situation. Recently, evolutionary algorithms (EAs) especially multi-objective evolutionary algorithms (MOEAs) have shown their competitiveness in addressing the problem of ROCCH maximization. Thus, a series of MOEAs with promising performance have been proposed to tackle it. However, designing a MOEA for high-dimensional ROOCH maximization is much more challenging due to the "curse of dimension". To this end, in this paper, an evolutionary multitasking approach (termed as EMT-ROCCH) is proposed, where a low-dimensional ROCCH maximization task T_{a} is constructed to assist the original high-dimensional task T_{o}. Specifically, in EMT-ROCCH, a low-dimensional assisted task T_{a} is firstly created. Then, two populations, P_{a} and P_{o}, are used to evolve tasks T_{a} and T_{o}, respectively. During the evolution, a knowledge transfer from P_{a} to P_{o} is designed to transfer the useful knowledge from P_{a} to accelerate the convergence of P_{o}. Moreover, a knowledge transfer from P_{o} to P_{a} is developed to utilize the useful knowledge in P_{o} to repair the individuals in P_{a}, aiming to avoid P_{a} being trapped into the local optima. Experiment re
ISSN:2471-285X
2471-285X
DOI:10.1109/TETCI.2024.3354101