2023
DOI: 10.21203/rs.3.rs-2434853/v1
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RETRACTED: Breast Cancer Classification using Improved Hybrid Model by Leveraging Slime Mould Algorithm and Whale Optimization Algorithm

Abstract: Breast Cancer Classification is important in the medical field for disease diagnosis and assists in decisions in treatment. Poor convergence and local optima are common limitations in the existing feature selection techniques. Overfitting and imbalance data problems are common limitations in existing classifiers. The hybrid method of Whale Optimization Algorithm (WOA) – Slime Mould Algorithm (SMA) is proposed for relevant feature selection and an Auto stacked encoder is applied for classification. The WOA tech… Show more

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Cited by 1 publication
(2 citation statements)
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“…Anji Reddy Vaka et al [91] proposed a hybrid SMA with WOA, where the WOA technique performed an exploration in the first half of the iterations and the SMA method performed an exploitation in the second half of the iterations. Bhandakkar AA et al [92] improved the searching behavior of an SMA by incorporating it with WOA.…”
Section: Hybridization With the Whale Optimization Algorithm (Woa)mentioning
confidence: 99%
See 1 more Smart Citation
“…Anji Reddy Vaka et al [91] proposed a hybrid SMA with WOA, where the WOA technique performed an exploration in the first half of the iterations and the SMA method performed an exploitation in the second half of the iterations. Bhandakkar AA et al [92] improved the searching behavior of an SMA by incorporating it with WOA.…”
Section: Hybridization With the Whale Optimization Algorithm (Woa)mentioning
confidence: 99%
“…Javidan SM et al [89] combined SM and SVM classifiers to diagnose three apple tree diseases. Anji reddy Vaka et al [91] proposed a hybrid WOA-SMA and applied it to BreakHis and IDC datasets to evaluate breast cancer classifications. Khan AA et al [100] presented a hybrid of an SMA with GWO for feature selection purpose and evaluated it in UCI repository datasets by comparing it to other algorithms.…”
Section: Feature Selection (Fs)mentioning
confidence: 99%