☆ 4.5 Article

Machine training and parameter settings with social emotional optimization algorithm for support vector machine

PATTERN RECOGNITION LETTERS (2015)

期刊

PATTERN RECOGNITION LETTERS

卷 54, 期 -, 页码 36-42

出版社

ELSEVIER

DOI: 10.1016/j.patrec.2014.11.011

关键词

Support vector machine; Social emotional optimization algorithm; Machine training; Parameter settings

类别

Computer Science, Artificial Intelligence

资金

Natural Science Foundation of China [50705097]
Tribology Science Foundation of State Key Laboratory of Tribology [SKLTKF09B06]

向作者/读者索取更多资源

Protocol

Reagent

摘要

Machine training along with the parameter settings significantly influences the performance of support vector machine (SVM). In this paper, the social emotional optimization algorithm (SEOA) characterized by excellent global optimization ability is employed for machine training and parameter settings for SVM. Instead of the quadratic programming problem, machine training for SVM is modeled as a multi-parameter optimization problem which is solved by SEOA. Besides, SEOA is also employed for SVM parameter settings. The kernel function parameter and error penalty parameter of SVM are simultaneously optimized by SEOA. The experiments adopt several real world datasets from the UCI database. The results indicate that training SVM with SEOA is feasible and effective. The trained SVM can achieve high classification accuracy while using fewer support vectors. Compared with cross validation method and PSO, SEOA is higher efficient in parameter settings of SVM. (C) 2014 Elsevier B.V. All rights reserved.

作者

我是这篇论文的作者

点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5

评分不足

次要评分

新颖性

-

重要性

-

科学严谨性

-

评价这篇论文

推荐

Article Computer Science, Artificial Intelligence

A fast parameter optimization approach based on the inter-cluster induced distance in the feature space for support vector machines

Jiapeng Wang, Jiaxiang Luo

Summary: This paper proposes a new method for optimizing the kernel and penalty parameters of SVM classifiers. The method introduces a new distance measure in the feature space and presents a fast parameter optimization approach that significantly reduces training time while maintaining competitive model accuracy.

APPLIED SOFT COMPUTING (2022)

添加到收藏夹

Article Quantum Science & Technology

Enhancing quantum support vector machines through variational kernel training

N. Innan, M. A. Z. Khan, B. Panda, M. Bennai

Summary: We propose a new model, quantum variational kernel SVM (QVK-SVM), in quantum machine learning (QML), which combines the quantum kernel and quantum variational algorithms to improve accuracy. extensive experiments on the Iris dataset demonstrate that QVK-SVM outperforms existing models regarding accuracy, loss, and confusion matrix indicators.

QUANTUM INFORMATION PROCESSING (2023)

添加到收藏夹

Article Computer Science, Information Systems

A bio-inspired fall webworm optimization algorithm for feature selection and support vector machine optimization for retinal abnormalities detection

B. Sakthi Karthi Durai, J. Benadict Raja

Summary: The early detection of retinal abnormalities like diabetic retinopathy (DR) can be achieved using computerized analysis of retinal fundus images. This study presents an automated process that employs an optimized SVM classifier and a new feature extraction method for more accurate and efficient detection of DR. The proposed technique is validated using a standard dataset and achieves high sensitivity, specificity, and accuracy.

MULTIMEDIA TOOLS AND APPLICATIONS (2023)

添加到收藏夹

Article Quantum Science & Technology

Research on support vector machine optimization based on improved quantum genetic algorithm

Fei Wang, Kunlun Xie, Lin Han, Menghui Han, Zeshi Wang

Summary: This paper introduces support vector machine and its problems, proposes an improved quantum genetic algorithm, and applies it to SVM parameter optimization. Experimental results show that the improved algorithm has better performance compared to other algorithms.

QUANTUM INFORMATION PROCESSING (2023)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Fast support vector machine training via three-term conjugate-like SMO algorithm

Lang Yu, Shengjie Li, Siyi Liu

Summary: A novel three-term conjugate-like sequential minimal optimization algorithm (TCSMO) is proposed for SVM classification and regression tasks, based on the conjugate sequential minimal optimization algorithm (CSMO). The TCSMO algorithm slightly increases the arithmetic operations but significantly reduces the number of iterations required for convergence, resulting in shorter training time for SVM. Numerical experiments demonstrate the improved performance of the TCSMO algorithm in both classification and regression tasks.

PATTERN RECOGNITION (2023)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Solution path algorithm for twin multi-class support vector machine

Liuyuan Chen, Kanglei Zhou, Junchang Jing, Haiju Fan, Juntao Li

Summary: This work proposes a fast regularization parameter tuning algorithm for the twin multi-class support vector machine. By adopting a novel sample data set partition strategy and utilizing linear equations and block matrix theory, the regularization parameters are continuously updated, and the relationship between the Lagrangian multipliers and the regularization parameters is proven. Finally, different events are defined to seek for the starting event for the next iteration, and the effectiveness of the proposed method is validated through experiments.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Simulation model and fault analysis of air circulation system of the aircraft based on grasshopper optimization algorithm: support vector machine

Wu Huiyong, Jin Shuchun, Jin Zhu

Summary: This paper analyzes the working state of the aircraft's air circulation system at high altitude and proposes a simulation analysis method based on simulated annealing-grasshopper optimization algorithm and support vector machine. By conducting system simulation and fault injection analysis, the results show that this method can effectively simulate temperature changes and distinguish fault types of the aircraft, and the convergence speed of the system is accelerated to avoid local optimal problems.

SOFT COMPUTING (2023)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Quantum-Inspired Support Vector Machine

Chen Ding, Tian-Yi Bao, He-Liang Huang

Summary: The study proposes a quantum-inspired classical algorithm for LS-SVM, utilizing an improved sampling technique for classification. The theoretical analysis indicates that the algorithm can achieve classification with logarithmic runtime for low-rank, low-condition number, and high-dimensional data matrices.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

添加到收藏夹

Article Computer Science, Information Systems

DC programming for sparse proximal support vector machines

Guoquan Li, Linxi Yang, Zhiyou Wu, Changzhi Wu

Summary: Proximal support vector machine (PSVM) is a variant of support vector machine (SVM) which aims to generate a pair of non-parallel hyperplanes for classification. Introducing l(0)-norm regularization in PSVM enables simultaneous selection of important features and removal of redundant features for classification. The proposed method utilizes a continuous nonconvex function and difference of convex functions algorithms (DCA) to solve the optimization problem efficiently.

INFORMATION SCIENCES (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Evolving data-adaptive support vector machines for binary classification

Wojciech Dudzik, Jakub Nalepa, Michal Kawulok

Summary: This paper addresses the optimization problem of SVMs for binary classification of difficult datasets, introducing an evolutionary technique and a co-evolutionary scheme. Experimental results show that the proposed algorithm outperforms popular supervised learners and other techniques for optimizing SVMs.

KNOWLEDGE-BASED SYSTEMS (2021)

添加到收藏夹

Article Automation & Control Systems

Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate

Jian Zhou, Yingui Qiu, Shuangli Zhu, Danial Jahed Armaghani, Chuanqi Li, Hoang Nguyen, Saffet Yagiz

Summary: This research aims to optimize the hyper-parameters of the support vector machine technique through the use of three optimization algorithms, namely gray wolf optimization, whale optimization algorithm, and moth flame optimization, for predicting the advance rate of a tunnel boring machine in hard rock conditions. The results indicate that the moth flame optimization algorithm can better capture the hyperparameters of the SVM model in predicting TBM AR.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Multiple birth support vector machine based on dynamic quantum particle swarm optimization algorithm

Shifei Ding, Zichen Zhang, Yuting Sun, Songhui Shi

Summary: The parameters of MBSVM are currently determined mainly by experience or artificially specified by the grid method, which can lead to insufficient parameter selection. To address this issue, a dynamic quantum particle swarm optimization algorithm (DQPSO) is proposed to optimize the MBSVM parameters. Experimental results show that the DQPSO algorithm outperforms the classical QPSO algorithm in terms of optimization performance and convergence speed, and the proposed DQPSO-MBSVM algorithm effectively improves the classification performance of MBSVM.

NEUROCOMPUTING (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Multicycle disassembly-based decomposition algorithm to train multiclass support vector machines

Tong Gao, Hao Chen

Summary: In this study, a multicycle disassembly-based decomposition algorithm (MCD-DA) is proposed to efficiently solve the training problem of multiclass support vector machine (SVM). MCD-DA constructs a graph model to re-express the constraints in multiclass SVM, partitions the complex feasible region into simple sub-feasible regions, and designs multiple cycle-based disassembly strategies to update the working variables analytically. Experimental results demonstrate that MCD-DA outperforms typical optimization algorithms for more sample cases.

PATTERN RECOGNITION (2023)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A hyper-parameter tuning approach for cost-sensitive support vector machine classifiers

Rosita Guido, Maria Carmela Groccia, Domenico Conforti

Summary: Hyperparameter tuning is essential for improving model performance in machine learning. This research focuses on classifying imbalanced data using cost-sensitive support vector machines and proposes a multi-objective approach to optimize the model's hyperparameters. The algorithm is presented in a basic version and an improved version utilizing genetic algorithms and decision trees. Experimental results demonstrate the importance of using appropriate evaluation measures for assessing the classification performance of imbalanced data classification models.

SOFT COMPUTING (2023)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A fast learning algorithm for One-Class Slab Support Vector Machines

Bagesh Kumar, Ayush Sinha, Sourin Chakrabarti, O. P. Vyas

Summary: In this paper, a fast training method for OCSSVM is proposed, which enhances its scalability without compromising precision significantly. Experimental results show that the proposed method achieves the best tradeoff between training time and accuracy, providing similar accuracies to regular OCSSVM and better scalability compared to existing state-of-the-art one-class classifiers.

KNOWLEDGE-BASED SYSTEMS (2021)

添加到收藏夹

暂无数据

暂无数据

© Peeref 2019-2024. All rights reserved.