Research on Parameters Optimization of SVM Based on Improved Fruit Fly Optimization Algorithm

Zhang, Qiantu; Fang, Liqing; Ma, Leilei; Zhao, Yulong

doi:10.7763/ijcte.2016.v8.1096

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…It is a good choice when there is no prior knowledge about the data. The RBF kernel performs a non-linear mapping of samples to a higherdimensional space, effectively handling situations where the relationship between class labels and attributes is not linear, unlike the linear kernel [80].…”

Section: Radial Basis Function (Rbf)mentioning

confidence: 99%

Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia

et al. 2023

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Vegetation plays an active role in ecosystem dynamics, and monitoring its patterns and changes is vital for effective environmental resource management. This study explores the possibility of machine learning techniques and remote sensing data to improve the accuracy of forest detection. The research focuses on the southeastern part of the Republic of Serbia as a case study area, using Sentinel-2 multispectral bands. The study employs publicly accessible satellite data and incorporates different vegetation indices to improve classification accuracy. The main objective is to examine the practicability of expanding the input parameters for forest detection using a machine learning approach. The classification process is performed by employing support vector machines (SVM) algorithm and utilising the SVM module in the scikit-learn package. The results demonstrate that including vegetation indices alongside the multispectral bands significantly improves the accuracy of vegetation detection. A comprehensive assessment reveals an overall classification accuracy of up to 99.01% when the selected vegetation indices (MCARI, RENDVI, NDI45, GNDVI, NDII) are combined with the Sentinel-2 bands. This research highlights the potential of machine learning and remote sensing in forest detection and monitoring. The findings underscore the importance of incorporating vegetation indices to enhance classification accuracy using the Python programming language. The study’s outcomes provide valuable insights for environmental resource management and decision-making processes, particularly in regions with diverse forest ecosystems.

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Section: Radial Basis Function (Rbf)mentioning

confidence: 99%

Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia

et al. 2023

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“…The constraint is changed to include these cases or points, too. The nonlinear case problem is formulated as follows [38][39]…”

Section: Support Vector Machinementioning

confidence: 99%

Kernel Parameter Optimization for Support Vector Machine Based on Sliding Mode Control

et al. 2022

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Support Vector Machine (SVM) is a supervised machine learning algorithm, which is used for robust and accurate classification. Despite its advantages, its classification speed deteriorates due to its large number of support vectors when dealing with large scale problems and dependency of its performance on its kernel parameter. This paper presents a kernel parameter optimization algorithm for Support Vector Machine (SVM) based on Sliding Mode Control algorithm in a closed-loop manner. The proposed method defines an error equation and a sliding surface, iteratively updates the Radial Basis Function (RBF) kernel parameter or the 2-degree polynomial kernel parameters, forcing SVM training error to converge below a threshold value. Due to the closed-loop nature of the proposed algorithm, key features such as robustness to uncertainty and fast convergence can be obtained. To assess the performance of the proposed technique, ten standard benchmark databases covering a range of applications were used. The proposed method and the state-of-theart techniques were then used to classify the data. Experimental results show the proposed method is significantly faster and more accurate than the anchor SVM technique and some of the most recent methods. These achievements are due to the closed-loop nature of the proposed algorithm, which significantly has reduced the data dependency of the proposed method.INDEX TERMS Support vector machine, Sliding mode control, RBF kernel, 2-degree polynomial kernel, Optimal parameter, Classification speed.

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Adaptive pairing of classifier and imputation methods based on the characteristics of missing values in data sets

Sim

Kwon

Lee

2016

Expert Systems with Applications

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Research on Parameters Optimization of SVM Based on Improved Fruit Fly Optimization Algorithm

Cited by 4 publications

References 12 publications

Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia

Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia

Kernel Parameter Optimization for Support Vector Machine Based on Sliding Mode Control

Adaptive pairing of classifier and imputation methods based on the characteristics of missing values in data sets

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