Article
Computer Science, Information Systems
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)
Article
Computer Science, Artificial Intelligence
Mohammed Alshutbi, Zhiyong Li, Moath Alrifaey, Masoud Ahmadipour, Muhammad Murtadha Othman
Summary: The decisions of experts and the evaluation of patient data play crucial roles in breast cancer analysis. Machine learning techniques can aid in quickly examining and diagnosing medical data, reducing potential errors caused by inexperienced decision-makers. This study proposes an intelligent cancer classification method that selects a feature subset and optimizes the parameters of the SVM classifier using the Jaya algorithm. The method is applied to accurately characterize a breast cancer dataset and compared with other classifiers, demonstrating its effectiveness.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Biology
Jianfu Xia, Zhifei Wang, Daqing Yang, Rizeng Li, Guoxi Liang, Huiling Chen, Ali Asghar Heidari, Hamza Turabieh, Majdi Mafarja, Zhifang Pan
Summary: This research aimed to construct a new intelligent diagnostic method that is accurate, fast, noninvasive, and cost-effective in distinguishing between complicated and uncomplicated appendicitis. The study analyzed the data of 298 patients with acute appendicitis and identified the most significant variables, then built a diagnostic model using an improved grasshopper optimization algorithm-based support vector machine.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Quantum Science & Technology
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)
Article
Environmental Sciences
Wen-jing Niu, Zhong-kai Feng, Shu-shan Li, Hui-jun Wu, Jia-yang Wang
Summary: This paper proposes a practical machine learning model for short-term load prediction based on feature selection and parameter optimization. Experimental results show that the proposed model outperforms several conventional models in short-term load prediction, and the CSA method is an effective tool for determining parameter combinations.
ENVIRONMENTAL RESEARCH LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Javier Alcaraz, Martine Labbe, Mercedes Landete
Summary: This paper introduces a Support Vector Machine with feature selection and proposes a bi-objective evolutionary algorithm to approximate the Pareto optimal frontier. Extensive computational experiments are conducted to compare the results obtained by different methods, and the properties of points in the Pareto frontier are discussed.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Dzelila Mehanovic, Dino Keco, Jasmin Kevric, Samed Jukic, Adnan Miljkovic, Zerina Masetic
Summary: This study migrates genetic algorithm-based feature selection methods to a MapReduce implementation suitable for parallelization on a high number of commodity hardware units, achieving significant practical and theoretical impact. The parallelization of genetic algorithm allows for randomness-enhanced feature selection, reducing overall data preprocessing time and leading to better feature selection, outperforming existing methods in practice.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Zhongjie Zhuang, Jeng-Shyang Pan, Junbao Li, Shu-Chuan Chu
Summary: Arithmetic Optimization Algorithm (AOA) is a simple and easy to implement algorithm with few parameters. It utilizes the distribution behavior of arithmetic operators in mathematics. In this manuscript, AOA algorithm is converted into binary form with improved exploration using Multiplication Mathematical Optimizer Operator (MOO). Four families of transfer functions are used in the binary AOA (BAOA). Parallel mechanism is introduced to further enhance performance and proposed the Parallel Binary AOA (PBAOA) algorithm. Experimental results show that the proposed BAOA and PBAOA algorithms outperform classical and state-of-the-art algorithms in feature selection problems on low-dimensional and high-dimensional datasets.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yinan Guo, Zirui Zhang, Fengzhen Tang
Summary: Feature selection is important in machine learning to reduce complexity and simplify interpretation. A novel non-linear method proposed in this paper uses kernelized multi-class support vector machines and fast recursive feature elimination to select features that work well for all classes, resulting in lower computational time complexity.
PATTERN RECOGNITION
(2021)
Article
Mathematics
Behzad Pirouz, Behrouz Pirouz
Summary: This article discusses the design of linear Support Vector Machine (SVM) classification techniques as multi-objective optimization problems. The authors focus on applying sparse optimization to feature selection for multi-objective optimization linear SVM. They emphasize the advantages of considering linear SVM classification techniques as multi-objective optimization problems.
Article
Computer Science, Artificial Intelligence
Aurora Saibene, Francesca Gasparini
Summary: In this study, a Genetic Algorithm (GA) was proposed for feature selection in EEG signals. Three different fitness functions were used without relying on expert knowledge. The results showed that the proposed method outperformed benchmarking techniques, especially in handling heterogeneous data and reducing feature dimensionality. Future works will focus on the optimization of GA parameters and the hybrid use of the two approaches.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
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)
Article
Biology
Mingjing Wang, Yingqi Liang, Zhongyi Hu, Siyuan Chen, Beibei Shi, Ali Asghar Heidari, Qian Zhang, Huiling Chen, Xiaowei Chen
Summary: This research aims to build a framework for discriminating between different types of lupus nephritis using real clinical data. By combining a hybrid stochastic optimizer moth-flame algorithm with support vector machine, a more stable and effective computer-assisted technique for analyzing systemic lupus erythematosus nephritis is developed.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Tengku Mazlin Tengku Ab Hamid, Roselina Sallehuddin, Zuriahati Mohd Yunos, Aida Ali
Summary: Discovering a hearing disorder early is crucial to reduce its effects, and approaches to improve remaining hearing ability are important for successful human communication development. The complexity posed by explosive dataset features makes it challenging to determine proper treatment. Irrelevant features and improper classifier parameters can significantly impact the accuracy of the audiometry system. This study proposes an ensemble filters feature selection method based on Information Gain, Gain Ratio, Chi-squared, and Relief-F, with optimization using Particle Swarm Optimization and Support Vector Machine. The results demonstrate that this method effectively handles high-dimensional data for hearing disorder prediction, achieving 96.50% accuracy compared to classical SVM.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Management
In Gyu Lee, Sang Won Yoon, Daehan Won
Summary: This article introduces a cost-effective 1-norm support vector machine with group feature selection to address the cost uncertainty of features. It also proposes an efficient algorithm for solving the models. Experimental results show that the model achieves competitive outcomes in terms of prediction accuracy and economic performance.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Sha Ding, Zhi Chen, Shi-yuan Zhao, Tao Lin
NEURAL PROCESSING LETTERS
(2018)
Article
Computer Science, Artificial Intelligence
Xin Xia, Tao Lin, Zhi Chen
APPLIED INTELLIGENCE
(2018)
Article
Computer Science, Artificial Intelligence
Zhi Chen, Tao Lin, Xin Xia, Hongyan Xu, Sha Ding
APPLIED INTELLIGENCE
(2018)
Article
Computer Science, Cybernetics
Zhi Chen, Tao Lin
BEHAVIOUR & INFORMATION TECHNOLOGY
(2017)
Article
Computer Science, Artificial Intelligence
Zhi Chen, Tao Lin, Rui Chen, Yingtao Xie, Hongyan Xu
APPLIED INTELLIGENCE
(2017)