Breast Cancer Diagnosis Approach Based on Meta-Heuristic Optimization Algorithm Inspired by the Bubble-Net Hunting Strategy of Whales

Sayed, Gehad Ismail; Darwish, Ashraf; Hassanien, Aboul Ella; Pan, Jeng‐Shyang

doi:10.1007/978-3-319-48490-7_36

Cited by 19 publications

(9 citation statements)

References 13 publications

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“…In machine learning, whale optimization algorithm (WOA) was proposed and applied for the selection of best feature subsets for classification purposes of the Wisconsin Breast Cancer Database with 32 attributes and 596 instances [27]. The proposed model involved the use of WOA bio-inspired algorithm to select salient features, and the selected features were fed into the SVM using different kernel functions including RBF, linear, polynomial and quadratic.…”

Section: Recent Applications Of Woamentioning

confidence: 99%

“…Thus, a constant update through the use of optimization algorithms is required over time. Features selection is among the most efficient means of improving system performance and enhancing data representation in terms of defined criteria [27]. It aims to determine the important features and remove the not so important ones from the actual dataset features.…”

Section: Introductionmentioning

confidence: 99%

“…Metaheuristic optimization algorithms are getting more and more popular in different machine learning applications due to the fact that they: (i) rely on rather simple concepts and are easy to implement; (ii) do not require gradient information; (iii) can bypass local optima; (iv) can be utilized in a wide range of problems covering different disciplines. Nature-inspired metaheuristic algorithms solve optimization problems by mimicking biological or physical phenomena [27].…”

Section: Introductionmentioning

confidence: 99%

“…It reduces the computational time for highly complex problems. The WOA has been used to enhance performances of classical engineering and machine learning problems [2,6,12,15,27,35]. However, the WOA is yet to be used on email spam datasets.…”

Section: Introductionmentioning

confidence: 99%

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Whale optimization algorithm-based email spam feature selection method using rotation forest algorithm for classification

et al. 2019

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Email has continued to be an integral part of our lives and as a means for successful communication on the internet. The problem of spam mails occupying a huge amount of space and bandwidth, and the weaknesses of spam filtering techniques which includes misclassification of genuine emails as spam (false positives) are a growing challenge to the internet world. This research work proposed the use of a metaheuristic optimization algorithm, the whale optimization algorithm (WOA), for the selection of salient features in the email corpus and rotation forest algorithm for classifying emails as spam and non-spam. The entire datasets were used, and the evaluation of the rotation forest algorithm was done before and after feature selection with WOA. The results obtained showed that the rotation forest algorithm after feature selection with WOA was able to classify the emails into spam and non-spam with a performance accuracy of 99.9% and a low FP rate of 0.0019. This shows that the proposed method had produced a remarkable improvement as compared with some previous methods.

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Section: Recent Applications Of Woamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Whale optimization algorithm-based email spam feature selection method using rotation forest algorithm for classification

et al. 2019

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“…as it has been tested with 29 mathematical benchmark and 6 structural design problem [18]. This method also has been successfully applied in many field such as machinery diagnosis [19,20], brain tumour [21], and breast cancer and tumour [22,23].…”

Section: Introductionmentioning

confidence: 99%

Optimized ELM based on Whale Optimization Algorithm for gearbox diagnosis

et al. 2019

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Extreme learning machine (ELM) is a fast and quick learning algorithm with better generalization performance. However, the randomness of input weight and hidden layer bias may affect the overall performance of ELM. This paper proposed a new approach to determine the optimized values of input weight and hidden layer bias for ELM using whale optimization algorithm (WOA), which we call WOA-ELM. An online gearbox vibration signals is used in this study. Empirical mode decomposition (EMD) and complementary mode decomposition (CEEMD) are used to decompose the signals into sub-signals known as intrinsic mode functions (IMFs). Then, statistical features are extracted from selected IMFs. WOA-ELM is used for classification of healthy and faulty condition of gearbox. The result shows that WOA-ELM provide better classification result as compared with conventional ELM. Therefore, this study provide a new diagnosis approach for gearbox application.

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A Novel Hybrid Extreme Learning Machine-Whale Optimization Algorithm for Bearing Fault Diagnosis

Isham

Saufi

Amirul³

2022

Lecture Notes in Electrical Engineering

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Breast Cancer Diagnosis Approach Based on Meta-Heuristic Optimization Algorithm Inspired by the Bubble-Net Hunting Strategy of Whales

Cited by 19 publications

References 13 publications

Whale optimization algorithm-based email spam feature selection method using rotation forest algorithm for classification

Whale optimization algorithm-based email spam feature selection method using rotation forest algorithm for classification

Optimized ELM based on Whale Optimization Algorithm for gearbox diagnosis

A Novel Hybrid Extreme Learning Machine-Whale Optimization Algorithm for Bearing Fault Diagnosis

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