Journal Article10.1016/j.knosys.2022.109611
Evolving ensembles using multi-objective genetic programming for imbalanced classification
L. Zhang,Kefan Wang,Luyuan Xu,Wen-Chao Sheng,Qiu Kang +4 more
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TL;DR: In this paper , an efficient evolutionary strategy with non-dominated sorting, environmental selection, and an archiving mechanism is designed to optimize the false positive rate, the false negative rate and reduce the size of the resulting tree.
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Abstract: Multi-objective Genetic Programming (MGP) plays a prominent role in generating Pareto optimal classifier sets and making trade-offs among multiple classes adaptively. However, the existing MGP algorithms show poor performance and are difficult to implement when dealing with imbalanced classification problems. This work proposes a new MGP-based algorithm designed for imbalanced classification. Firstly, an efficient evolutionary strategy with nondominated sorting, environmental selection, and an archiving mechanism is designed to optimize the false positive rate, the false negative rate and reduce the size of the resulting tree. Then, a weighted ensemble decision is made according to each classifier’s performance in the majority and minority classes to obtain final classification results. Experimental results on 21 binary-class datasets and 17 multi-class datasets show that the proposed method outperforms existing ones in several commonly used imbalanced classification metrics.
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Citations
Minority-Weighted Graph Neural Network for Imbalanced Node Classification in Social Networks of Internet of People
01 Jan 2023
TL;DR: Wang et al. as mentioned in this paper proposed a minority-weighted graph neural network (mGNN), which extends imbalanced classification ideas in the traditional machine learning field to graph-structured data to improve the classification performance of graph neural networks.
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A Pearson correlation-based adaptive variable grouping method for large-scale multi-objective optimization
TL;DR: In this paper , the authors proposed a Pearson correlation-based adaptive variable grouping method, which not only consumes no additional computational budget, but also is able to adaptively divide variables with the evolvement of solutions.
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Domain Adaptation Multitask Optimization
01 Jul 2023
TL;DR: In this article , a domain adaptation-based mapping strategy was proposed to reduce the difference across solution domains and find more genetic traits to improve the effectiveness of information interactions in multi-task optimization.
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Domain Adaptation Multitask Optimization
TL;DR: In this article , a domain adaptation-based mapping strategy was proposed to reduce the difference across solution domains and find more genetic traits to improve the effectiveness of information interactions in multi-task optimization.
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A Line Complex-Based Evolutionary Algorithm for Many-Objective Optimization
TL;DR: Zhang et al. as discussed by the authors proposed to evolve solutions through line complex rather than solution points in Euclidean space, where Plücker coordinates are used to project solution points to line complex composed of position vectors and momentum vectors.
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References
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
SMOTE: synthetic minority over-sampling technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
•Journal Article
Statistical Comparisons of Classifiers over Multiple Data Sets
TL;DR: A set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers is recommended: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparisons of more classifiers over multiple data sets.
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
Kalyanmoy Deb,Himanshu Jain +1 more
TL;DR: A reference-point-based many-objective evolutionary algorithm that emphasizes population members that are nondominated, yet close to a set of supplied reference points is suggested that is found to produce satisfactory results on all problems considered in this paper.