A comprehensive health classification model based on support vector machine for proseal laryngeal mask and tracheal catheter assessment in herniorrhaphy

Du, Zhen; Qing, Yang; He, He Fan; Qiu, Ming; Chen, Zhi Yao; Hu, Qing Fu; Wang, Qing Mao; Zhang, Ziping; Lin, Qiong; Huang, Liu Yue; Huang, Ya Jiao

doi:10.3934/mbe.2020097

Cited by 1 publication

(3 citation statements)

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“…It can solve the shortcoming that the polynomial mutation method may fall into local optimum when the variable is close to the boundary [32]. Equations ( 9)- (12) show the process of HDPM changes the i x lb ub…”

Section: Highly Disruptive Polynomial Mutation (Hdpm)mentioning

confidence: 99%

“…The traditional polynomial mutation (PM) has almost no effect when the variable is close to the boundary. The HDPM strategy uses Equation (12) to generate the mutation location of the chimp, which helps further to explore the regions and boundaries of the initial space. In this way, even if the variables are on the search boundary, the search space can be fully utilized to ensure the diversity of the population.…”

Section: Improvement Strategymentioning

confidence: 99%

“…Vapnik and other researchers built the cornerstone of SVM [5]. It is widely used in pattern recognition [6,7], text recognition [8], biomedical [9][10][11], imaging medicine [12], anomaly detection [13] and in other fields. SVM classifies feature spaces into two categories and defines a non-parametric method for finding decision boundaries [14].…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous Feature Selection and Support Vector Machine Optimization Using an Enhanced Chimp Optimization Algorithm

Zhang

Jia

et al. 2021

Algorithms

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Chimp Optimization Algorithm (ChOA), a novel meta-heuristic algorithm, has been proposed in recent years. It divides the population into four different levels for the purpose of hunting. However, there are still some defects that lead to the algorithm falling into the local optimum. To overcome these defects, an Enhanced Chimp Optimization Algorithm (EChOA) is developed in this paper. Highly Disruptive Polynomial Mutation (HDPM) is introduced to further explore the population space and increase the population diversity. Then, the Spearman’s rank correlation coefficient between the chimps with the highest fitness and the lowest fitness is calculated. In order to avoid the local optimization, the chimps with low fitness values are introduced with Beetle Antenna Search Algorithm (BAS) to obtain visual ability. Through the introduction of the above three strategies, the ability of population exploration and exploitation is enhanced. On this basis, this paper proposes an EChOA-SVM model, which can optimize parameters while selecting the features. Thus, the maximum classification accuracy can be achieved with as few features as possible. To verify the effectiveness of the proposed method, the proposed method is compared with seven common methods, including the original algorithm. Seventeen benchmark datasets from the UCI machine learning library are used to evaluate the accuracy, number of features, and fitness of these methods. Experimental results show that the classification accuracy of the proposed method is better than the other methods on most data sets, and the number of features required by the proposed method is also less than the other algorithms.

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Section: Highly Disruptive Polynomial Mutation (Hdpm)mentioning

confidence: 99%

Section: Improvement Strategymentioning

confidence: 99%