2021
DOI: 10.1007/s40747-021-00314-z
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Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

Abstract: Many real-life datasets are imbalanced in nature, which implies that the number of samples present in one class (minority class) is exceptionally less compared to the number of samples found in the other class (majority class). Hence, if we directly fit these datasets to a standard classifier for training, then it often overlooks the minority class samples while estimating class separating hyperplane(s) and as a result of that it missclassifies the minority class samples. To solve this problem, over the years,… Show more

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Cited by 19 publications
(10 citation statements)
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“…This was based on the hypothesis that a good choice for a missing value would be the one that can reconstruct itself from the autoencoder. Recently, Shaw et al [202,203] used methods to handle imbalance class problems and subsequently improved the cancer prediction performance. In the work [203], the authors proposed an ensemble approach to handle the class problem while in [202] they solved the problem using an evolutionary algorithm.…”
Section: Diagnosis Report Based Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…This was based on the hypothesis that a good choice for a missing value would be the one that can reconstruct itself from the autoencoder. Recently, Shaw et al [202,203] used methods to handle imbalance class problems and subsequently improved the cancer prediction performance. In the work [203], the authors proposed an ensemble approach to handle the class problem while in [202] they solved the problem using an evolutionary algorithm.…”
Section: Diagnosis Report Based Methodsmentioning
confidence: 99%
“…Recently, Shaw et al [202,203] used methods to handle imbalance class problems and subsequently improved the cancer prediction performance. In the work [203], the authors proposed an ensemble approach to handle the class problem while in [202] they solved the problem using an evolutionary algorithm. In [202], the ring theory-based algorithm was hybridized with the PSO algorithm to select the near-optimal majority class samples from the training set.…”
Section: Diagnosis Report Based Methodsmentioning
confidence: 99%
“…9 Meta-heuristic algorithms 42,43 are computational intelligence paradigms mainly used for solving different complex optimization problems. Owing to their computational efficiency as well as superior performance in resource-constrained environments, meta-heuristic algorithms have been extensively used across the domains, including feature selection, 44 neural architecture search, 45,46 task scheduling, 47 handwritten script classification, 48 image contrast enhancement, [49][50][51] data clustering, 52 multilevel image thresholding, 53,54 and solving class imbalance problem 55,56 among others. Mostly, these algorithms are inspired from: (1) theory of evolution, such as Genetic Algorithm (GA) 57 and Differential Evolution 58 ; (2) natural behavior of organisms, such as the Whale Optimization Algorithm (WOA), 59 Cuckoo Search (CS) 60 and Flower Pollination Algorithm 61 ; (3) swarm intelligence, such as the Particle Swarm Optimization (PSO) 62 and the Grey Wolf Optimizer (GWO) 63 ; and (4) physical or scientific phenomena, such as the Gravitational Search Algorithm (GSA), 64 and the Multiverse Optimizer, 65 to name a few.…”
Section: Related Workmentioning
confidence: 99%
“…The tuning between these two phases is really important for obtaining remarkable results. As mentioned earlier, meta-heuristics are used for tackling various problems which comes under the optimization category such as image contrast enhancement [1], [13] etc, for solving feature selection problem [19], [2], [41] etc, for solving class imbalance problem [40] etc, and many more.…”
Section: Introductionmentioning
confidence: 99%