Improved Whale optimization Algorithm for SVM Model Selection: Application in Medical Diagnosis

Chaabane, Sarra Ben; Kharbech, Sofiane; Belazi, Akram; Bouallègue, Ammar

doi:10.23919/softcom50211.2020.9238265

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…The efficacy of the proposed approach is assessed by relating the numerical results with that of nine similar approaches, including conventional SVM [32], CSA-SVM [35], GA-SVM [41], PSO-SVM [42], Improved whale optimization algorithm-based SVM (IWOA-SVM) [43], Differential Evolution algorithm-based SVM (DE-SVM) [44], GWO-SVM [32], Enhanced GWO-based SVM (EGWO-SVM) [45], Augmented GWO-based SVM (AGWO-SVM) [46]. In this study, LIBSVM model introduced by Chang and Lin [47] is used to realize SVM.…”

Section: Resultsmentioning

confidence: 99%

A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

Mallika

Selvamuthukumaran²

2021

Int J Comput Intell Syst

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Diabetes is an extremely serious hazard to global health and its incidence is increasing vividly. In this paper, we develop an effective system to diagnose diabetes disease using a hybrid optimization-based Support Vector Machine (SVM).The proposed hybrid optimization technique integrates a Crow Search algorithm (CSA) and Binary Grey Wolf Optimizer (BGWO) for exploiting the full potential of SVM in the diabetes diagnosis system. The effectiveness of our proposed hybrid optimization-based SVM (hereafter called CS-BGWO-SVM) approach is carefully studied on the real-world databases such as UCIPima Indian standard dataset and the diabetes type dataset from the Data World repository. To evaluate the CS-BGWO-SVM technique, its performance is related to several state-of-the-arts approaches using SVM with respect to predictive accuracy, Intersection Over-Union (IoU), specificity, sensitivity, and the area under receiver operator characteristic curve (AUC). The outcomes of empirical analysis illustrate that CS-BGWO-SVM can be considered as a more efficient approach with outstanding classification accuracy. Furthermore, we perform the Wilcoxon statistical test to decide whether the proposed cohesive CS-BGWO-SVM approach offers a substantial enhancement in terms of performance measures or not. Consequently, we can conclude that CS-BGWO-SVM is the better diabetes diagnostic model as compared to modern diagnosis methods previously reported in the literature.

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Section: Resultsmentioning

confidence: 99%

A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

Mallika

Selvamuthukumaran²

2021

Int J Comput Intell Syst

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“…are good candidates for optimizing and selecting the SVM model parameters. For instance, an improved WOA algorithm was proposed to reach the optimal parameters of the SVM model [15]. Liu et al [16] extracted multi-domain features of sEMG signals, and simultaneously established an accurate SVM classification model by proposing an improved WOA.…”

Section: Introductionmentioning

confidence: 99%

Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography

Tu,

Li,

Wang

2024

Sensors

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During robot-assisted rehabilitation, failure to recognize lower limb movement may efficiently limit the development of exoskeleton robots, especially for individuals with knee pathology. A major challenge encountered with surface electromyography (sEMG) signals generated by lower limb movements is variability between subjects, such as motion patterns and muscle structure. To this end, this paper proposes an sEMG-based lower limb motion recognition using an improved support vector machine (SVM). Firstly, non-negative matrix factorization (NMF) is leveraged to analyze muscle synergy for multi-channel sEMG signals. Secondly, the multi-nonlinear sEMG features are extracted, which reflect the complexity of muscle status change during various lower limb movements. The Fisher discriminant function method is utilized to perform feature selection and reduce feature dimension. Then, a hybrid genetic algorithm-particle swarm optimization (GA-PSO) method is leveraged to determine the best parameters for SVM. Finally, the experiments are carried out to distinguish 11 healthy and 11 knee pathological subjects by performing three different lower limb movements. Results demonstrate the effectiveness and feasibility of the proposed approach in three different lower limb movements with an average accuracy of 96.03% in healthy subjects and 93.65% in knee pathological subjects, respectively.

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“…In the problem of the SVM classification of imbalanced datasets, authors in [3] suggested an approach to optimal parameters selection for the synthetic minority over-sampling technique algorithm. Authors in [4] proposed an improved version of the Whale Optimization Algorithm aiming to choose the best model for SVM by looking for the optimal parameter values. In [5], authors proposed a bioinspired optimization tool for SVM for hyperparameters tuning demonstrating better results in terms of speed and simplicity compared to state of art works.…”

Section: Introductionmentioning

confidence: 99%

Efficient Machine Learning Algorithm for Embedded Tactile Data Processing

Saleh

Ibrahim

Menichelli

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Employing Machine learning algorithms in tactile sensing systems have emerged recently to recognize/classify touch patterns. The high computational complexity of the ML algorithms makes challenging the embedded implementation of tactile data processing. This paper proposes a complexity optimized tensorial-based machine learning algorithm for touch modality classification. The aim is to introduce an efficient algorithm minimizing the system complexity in terms of number of operations and memory storage which directly affect time latency and power consumption. With respect to the state of the art, the proposed approach reduces the number of operations per inference from 545 M-ops to 18 M-ops and the memory storage from 52.2 KB to 1.7 KB. Moreover, the proposed method speeds up the inference time by a factor of 43× at a cost of only 2% loss in accuracy.

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Improved Whale optimization Algorithm for SVM Model Selection: Application in Medical Diagnosis

Cited by 8 publications

References 13 publications

A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography

Efficient Machine Learning Algorithm for Embedded Tactile Data Processing

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