SVM Multi-Classification Optimization Research based on Multi-Chromosome Genetic Algorithm

Ren, Qiushi

doi:10.23940/ijpe.18.04.p5.631638

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Although the procedure of the grid search method is simple to understand and perform, it costs a lot of computation resources and the optimization efficiency is below expectation [4]. The genetic algorithm is a good method for the optimization of the SVM parameters [5,6]. Compared to the genetic algorithm, the PSO algorithm can usually provide better fitness values with a less complexity of optimization [7,8].…”

Section: Introductionmentioning

confidence: 99%

An Improved Skewness Decision Tree SVM Algorithm for the Classification of Steel Cord Conveyor Belt Defects

Mao

Zhang

et al. 2018

Applied Sciences

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Skewness Decision Tree Support Vector Machine (SDTSVM) algorithm is widely known as a supervised learning model for multi-class classification problems. However, the classification accuracy of the SDTSVM algorithm depends on the perfect selection of its parameters and the classification order. Therefore, an improved SDTSVM (ISDTSVM) algorithm is proposed in order to improve the classification accuracy of steel cord conveyor belt defects. In the proposed model, the classification order is determined by the sum of the Euclidean distances between multi-class sample centers and the parameters are optimized by the inertia weight Particle Swarm Optimization (PSO) algorithm. In order to verify the effectiveness of the ISDTSVM algorithm with different feature space, experiments were conducted on multiple UCI (University of California Irvine) data sets and steel cord conveyor belt defects using the proposed ISDTSVM algorithm and the conventional SDTSVM algorithm respectively. The average classification accuracies of five-fold cross-validation were obtained, based on two kinds of kernel functions respectively. For the Vowel, Zoo, and Wine data sets of the UCI data sets, as well as the steel cord conveyor belt defects, the ISDTSVM algorithm improved the classification accuracy by 3%, 3%, 1% and 4% respectively, compared to the SDTSVM algorithm. The classification accuracy of the radial basis function kernel were higher than the polynomial kernel. The results indicated that the proposed ISDTSVM algorithm improved the classification accuracy significantly, compared to the conventional SDTSVM algorithm.

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Section: Introductionmentioning

confidence: 99%

An Improved Skewness Decision Tree SVM Algorithm for the Classification of Steel Cord Conveyor Belt Defects

Mao

Zhang

et al. 2018

Applied Sciences

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Application of LightGBM Algorithm in the Initial Design of a Library in the Cold Area of China Based on Comprehensive Performance

Zhou

Wang

et al. 2022

Buildings

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The proper application of machine learning and genetic algorithms in the early stage of library design can obtain better all-around building performance. The all-around performance of the library, such as indoor temperature, solar radiation, indoor lighting, etc., must be fully considered in the initial design stage. Aiming at building performance optimization and based on the method of “generative design”, this paper constructs the library’s comprehensive performance evaluation workflow and rapid prediction combined with the LightGBM algorithm. A library in a cold region of China is taken as the research object to verify its application. In this study, 5000 scheme samples generated in the iterative genetic optimization process were taken as data sets. The LightGBM algorithm was used to classify and predict design schemes, with a precision of 0.78, recall rate of 0.93, and F1-Score of 0.851. This method can help architects to fully exploit the optimization potential of the building’s all-around performance in the initial stage of library design and ensure the timely interaction and feedback between design decisions and performance evaluation.

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SVM Multi-Classification Optimization Research based on Multi-Chromosome Genetic Algorithm

Cited by 2 publications

References 16 publications

An Improved Skewness Decision Tree SVM Algorithm for the Classification of Steel Cord Conveyor Belt Defects

An Improved Skewness Decision Tree SVM Algorithm for the Classification of Steel Cord Conveyor Belt Defects

Application of LightGBM Algorithm in the Initial Design of a Library in the Cold Area of China Based on Comprehensive Performance

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