GA-SVM based feature selection and parameter optimization in hospitalization expense modeling

Tao, Zhou; Lu, Huiling; Wang, Wenwen; Xia, Yong

doi:10.1016/j.asoc.2018.11.001

Cited by 192 publications

(69 citation statements)

References 13 publications

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“…[20] used Ant Colony Optimization (ACO) for the FS problem. [21] used the Genetic Algorithm(GA) for the FS problem. [4,22] improved Whale Optimization Algorithm (WOA) for the FS problem.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Butterfly Optimization Algorithm for Feature Selection

et al. 2020

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Feature selection represents an essential pre-processing step for a wide range of Machine Learning approaches. Datasets typically contain irrelevant features that may negatively affect the classifier performance. A feature selector can reduce the number of these features and maximise the classifier accuracy. This paper proposes a Dynamic Butterfly Optimization Algorithm (DBOA) as an improved variant to Butterfly Optimization Algorithm (BOA) for feature selection problems. BOA represents one of the most recently proposed optimisation algorithms. BOA has demonstrated its ability to solve different types of problems with competitive results compared to other optimisation algorithms. However, the original BOA algorithm has problems when optimising high-dimensional problems. Such issues include stagnation into local optima and lacking solutions diversity during the optimisation process. To alleviate these weaknesses of the original BOA, two significant improvements are introduced in the original BOA: the development of a Local Search Algorithm Based on Mutation (LSAM) operator to avoid local optima problem and the use of LSAM to improve BOA solutions diversity. To demonstrate the efficiency and superiority of the proposed DBOA algorithm, 20 benchmark datasets from the UCI repository are employed. The classification accuracy, the fitness values, the number of selected features, the statistical results, and convergence curves are reported for DBOA and its competing algorithms. These results demonstrate that DBOA significantly outperforms the comparative algorithms on the majority of the used performance metrics. INDEX TERMS Butterfly optimisation algorithm, feature selection, local search algorithm based on mutation.

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“…[20] used Ant Colony Optimization (ACO) for the FS problem. [21] used the Genetic Algorithm(GA) for the FS problem. [4,22] improved Whale Optimization Algorithm (WOA) for the FS problem.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Butterfly Optimization Algorithm for Feature Selection

et al. 2020

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“…Detailed modeling methods are showed as below [60,61]: https://doi.org/10.1371/journal.pone.0235780.g005…”

Section: Evaluation Modelsmentioning

confidence: 99%

Susceptibility mapping and zoning of highway landslide disasters in China

Yin

Li²,

Che

et al. 2020

PLoS ONE

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Prominent regional differentiations of highway landslide disasters (HLDs) bring great difficulties in highway planning, designing and disaster mitigation, therefore, a comprehensive understanding of HLDs from the spatial perspective is a basis for reducing damages. Statistical prediction methods and machine learning methods have some defects in landslide susceptibility mapping (LSM), meanwhile, hybrid methods have been developed by combining the statistical prediction methods with machine learning methods in recent years, and some of them were reported to perform better than conventional methods. In view of this, the principal component analysis (PCA) method was used to extract the susceptibility evaluation indexes of HLDs; the particle swarm optimization-support vector machine (PSO-SVM) model and genetic algorithm-support vector machine (GA-SVM) model were implemented to the susceptibility mapping and zoning of HLDs in China. The research results show that the accumulative contribution rate of the four principal components is 92.050%; evaluation results of the PSO-SVM model are better than those of the GA-SVM model; micro dangerous areas, moderate dangerous areas, severe dangerous areas and extreme dangerous areas account for 24.24%, 19.49%, 36.53% and 19.74% of the total areas of China; among the 1543 disaster points in the HLDs inventory, there are 134, 182, 421 and 806 located in the above areas respectively.

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“…In this study, 10 different classification algorithms are used to predict the differentiation of esophageal squamous cell carcinoma. Ten classification algorithms used in this paper are SVM (Vadali et al, 2019), Quadratic Discriminant Analysis (QDA) (Siqueira et al, 2017), CART (Cheng et al, 2017), Linear Discriminant Analysis (LDA) (Liu et al, 2016), KNN (Suyundikov et al, 2015), Ensemble (Xiao et al, 2018), ELM (Sachnev et al, 2015), Particle Swarm Optimization-Support Vector Machine (PSO-SVM) (Jiang et al, 2010), Genetic Algorithm-Support Vector Machine (GA-SVM) (Tao et al, 2019), and ABC-SVM (Alshamlan et al, 2016). Thirteen and twenty-one indicators are used as input characteristics, respectively.…”

Section: Correlation Indicators Validation and Escc Differentiation Pmentioning

confidence: 99%

Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree

et al. 2020

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The diagnosis of the degree of differentiation of tumor cells can help physicians to make timely detection and take appropriate treatment for the patient's condition. In this study, the original dataset is clustered into two independent types by the Kohonen clustering algorithm. One type is used as the development sets to find correlation indicators and establish predictive models of differentiation, while the other type is used as the validation sets to test the correlation indicators and models. In the development sets, thirteen indicators significantly associated with the degree of differentiation of esophageal squamous cell carcinoma are found by the Kohonen clustering algorithm. Thirteen relevant indicators are used as input features and the degree of tumor differentiations is used as output. Ten classification algorithms are used to predict the differentiation of esophageal squamous cell carcinoma. Artificial bee colony-support vector machine (ABC-SVM) predicts better than the other nine algorithms, with an average accuracy of 81.5% for the 10-fold cross-validation. Based on logistic regression and ReliefF algorithm, five models with the greater merit for the degree of differentiation are found in the development sets. The AUC values of the five models are 0.672, 0.628, 0.630, 0.628, and 0.608 (P < 0.05). The AUC values of the five models in the validation sets are 0.753, 0.728, 0.744, 0.776, and 0.868 (P < 0.0001). The predicted values of the five models are constructed as the input features of ABC-SVM. The accuracy of the 10-fold cross-validation reached 82.0 and 86.5% in the development sets and the validation sets, respectively.

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GA-SVM based feature selection and parameter optimization in hospitalization expense modeling

Cited by 192 publications

References 13 publications

Dynamic Butterfly Optimization Algorithm for Feature Selection

Dynamic Butterfly Optimization Algorithm for Feature Selection

Susceptibility mapping and zoning of highway landslide disasters in China

Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree

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