Article
Computer Science, Artificial Intelligence
An-Da Li, Bing Xue, Mengjie Zhang
Summary: This paper proposes an improved sticky binary PSO algorithm for feature selection problems, which aims to enhance evolutionary performance through new mechanisms such as an initialization strategy, dynamic bits masking, and genetic operations. Experimental results show that ISBPSO achieves higher accuracy with fewer features and reduces computation time compared to benchmark PSO-based FS methods.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Ibrahim M. EL-Hasnony, Mohamed Elhoseny, Zahraa Tarek
Summary: A novel hybrid feature selection approach combining the butterfly optimization algorithm (BOA) and particle swarm optimization (PSO) was developed to improve algorithm performance, with experimental results demonstrating superiority in classification precision and number of selected features.
Article
Computer Science, Artificial Intelligence
Jingwei Too, Seyedali Mirjalili
Summary: This article proposed a novel feature selection method HLBDA, using a hyper learning strategy to enhance the algorithm performance, and compared it with multiple datasets, demonstrating the superior effectiveness of HLBDA in improving classification accuracy and reducing the number of selected features.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jingwei Too, Ali Safaa Sadiq, Seyed Mohammad Mirjalili
Summary: This paper proposes a novel conditional opposition-based particle swarm optimization algorithm for feature selection. By introducing opposition-based learning and conditional strategy, the performance of the particle swarm optimization algorithm is improved. Experimental results demonstrate that the proposed approach not only achieves high prediction accuracy but also yields small feature sizes.
CONNECTION SCIENCE
(2022)
Article
Engineering, Electrical & Electronic
Yu Zhou, Lin Gao, Dong Wang, Wenhui Wu, Zhiqiang Zhou, Tingqun Ye
Summary: In this study, an improved localized feature selection method based on multiobjective binary particle swarm optimization was proposed to address fault diagnosis by utilizing the local distribution of data without the need for balancing strategies.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Ke Chen, Bing Xue, Mengjie Zhang, Fengyu Zhou
Summary: This article introduces a novel PSO-based feature selection approach that continuously improves population quality and performance through correlation-guided updating and surrogate technique. Experimental results demonstrate its outstanding performance in classification accuracy.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Biochemical Research Methods
T. I. M. E. A. BEZDAN, M. I. O. D. R. A. G. ZIVKOVIC, N. E. B. O. J. S. A. BACANIN, A. M. I. T. CHHABRA, M. U. T. H. U. S. A. M. Y. SURESH
Summary: Feature selection methods can reduce the dimension of high-dimensional data, improve prediction performance, and reduce computation time. In this article, a binary hybrid metaheuristic-based algorithm is proposed for feature selection, which is evaluated on multiple datasets and outperforms other methods in terms of classification accuracy.
JOURNAL OF COMPUTATIONAL BIOLOGY
(2022)
Article
Computer Science, Information Systems
An-Da Li, Bing Xue, Mengjie Zhang
Summary: This paper proposes a feature selection method to identify key quality features in complex manufacturing processes. A multi-objective binary particle swarm optimization algorithm is proposed, which includes three new components to optimize a bi-objective feature selection model. Experimental results show that this method can identify a small number of key quality features with good predictive ability.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Himansu Das, Bighnaraj Naik, H. S. Behera
Summary: This paper proposes a feature selection approach based on Jaya optimization algorithm, which improves the performance of supervised machine learning techniques by reducing the dimensions of the feature space. Experimental results show that this approach achieves higher classification accuracy compared to other feature selection methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Pradip Dhal, Chandrashekhar Azad
Summary: In this study, a binary version of the hybrid two-phase multi-objective FS approach based on PSO and GWO is proposed. The approach aims to minimize classification error rate and reduce the number of selected features. By utilizing global and local search strategies, the method shows efficient and effective performance in selecting prominent features in high-dimensional data.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
N. Eslami, S. Yazdani, M. Mirzaei, E. Hadavandi
Summary: The rapid development of intelligent technologies and gadgets has led to a significant increase in the dimensions of datasets. Feature selection methods, such as dimension reduction algorithms, are crucial for addressing this challenge. Metaheuristic algorithms, known for their acceptable computational cost and performance, have been extensively used in feature selection tasks. This article introduces a binary-modified version of aphid-ant mutualism (BAAM) for solving feature selection problems. BAAM, unlike its counterpart AAM, allows for changing the number of colonies' members in each iteration based on attraction power, and utilizes a random cross-over operator to maintain population diversity and prevent premature convergence. BAAM outperforms other feature selection algorithms in terms of classification accuracy, selecting the most informative features, and convergence speed, as shown by experiments on various benchmark datasets and a COVID-19 dataset.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Nagwan Abdel Samee, El-Sayed M. El-Kenawy, Ghada Atteia, Mona M. Jamjoom, Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid, Noha E. El-Attar, Tarek Gaber, Adam Slowik, Mahmoud Y. Shams
Summary: This study proposes a novel metaheuristic approach based on hybrid dipper throated and particle swarm optimizers for rapid and automatic detection of COVID-19 by analyzing the chest X-ray images. The experimental results show that the proposed method achieves an accuracy of 99.88%, outperforming existing COVID-19 detection models.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Automation & Control Systems
Bach Hoai Nguyen, Bing Xue, Peter Andreae, Mengjie Zhang
Summary: The key of applying PSO to binary problems lies in systematically exploring the relationships among velocity, momentum, exploration, and exploitation, helping evolve better solutions.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Engineering, Multidisciplinary
Jian Zhu, Jianhua Liu, Yuxiang Chen, Xingsi Xue, Shuihua Sun
Summary: The paper introduces the Binary Restructuring Particle Swarm Optimization (BRPSO) algorithm as an adaptation of the Restructuring Particle Swarm Optimization (RPSO) algorithm for solving discrete optimization problems. Unlike other binary metaheuristic algorithms, BRPSO does not use transfer functions, instead relying on comparison results and a novel perturbation term for the particle updating process. The algorithm requires fewer parameters and exhibits high exploration capability, as demonstrated by experiments on feature selection problems.
Article
Computer Science, Artificial Intelligence
Abdolreza Rashno, Milad Shafipour, Sadegh Fadaei
Summary: This paper introduces a novel multi-objective particle swarm optimization feature selection method. It decodes feature vectors as particles and ranks them in a two-dimensional optimization space. The proposed method incorporates feature ranks to update particle velocity and position during the optimization process. Experimental results demonstrate the effectiveness of the method in finding Pareto Fronts of the best particles in multi-objective optimization space.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Guang-Hai Liu, Zuo-Yong Li, Jing-Yu Yang, David Zhang
Summary: This article introduces a novel image retrieval method that improves retrieval performance by using sublimated deep features. The method incorporates orientation-selective features and color perceptual features, effectively mimicking these mechanisms to provide a more discriminating representation.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Fengguang Peng, Zihan Ding, Ziming Chen, Gang Wang, Tianrui Hui, Si Liu, Hang Shi
Summary: RGB-Thermal (RGB-T) semantic segmentation is an emerging task that aims to improve the robustness of segmentation methods under extreme imaging conditions by using thermal infrared modality. The challenges of foreground-background distinguishment and complementary information mining are addressed by proposing a cross modulation process with two collaborative components. Experimental results show that the proposed method achieves state-of-the-art performances on current RGB-T segmentation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Huikai Liu, Ao Zhang, Wenqian Zhu, Bin Fu, Bingjian Ding, Shengwu Xiong
Summary: Adverse weather conditions present challenges for computer vision tasks, and image de-weathering is an important component of image restoration. This paper proposes a multi-patch skip-forward structure and a Residual Deformable Convolutional module to improve feature extraction and pixel-wise reconstruction.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schonlieb, Matthew Thorpe, Konstantinos C. Zygalakis
Summary: The transportation LP distance (TLP) is a generalization of the Wasserstein WP distance that can be applied directly to color or multi-channelled images, as well as multivariate time-series. TLP interprets signals as functions, while WP interprets signals as measures. Although both distances are powerful tools in modeling data with spatial or temporal perturbations, their computational cost can be prohibitively high for moderate pattern recognition tasks. The linear Wasserstein distance offers a method for projecting signals into a Euclidean space, and in this study, we propose linear versions of the TLP distance (LTLP) that show significant improvement over the linear WP distance in signal processing tasks while being several orders of magnitude faster to compute than the TLP distance.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Haitao Tian, Shiru Qu, Pierre Payeur
Summary: This paper proposes a method of target-dependent classifier, which optimizes the joint hypothesis of domain adaptation into a target-dependent hypothesis that better fits with the target domain clusters through an unsupervised fine-tuning strategy and the concept of meta-learning. Experimental results demonstrate that this method outperforms existing techniques in synthetic-to-real adaptation and cross-city adaptation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Qingsen Yan, Axi Niu, Chaoqun Wang, Wei Dong, Marcin Wozniak, Yanning Zhang
Summary: Deep learning-based methods have achieved remarkable results in the field of super-resolution. However, the limitation of paired training image sets has led researchers to explore self-supervised learning. However, the assumption of inaccurate downscaling kernel functions often leads to degraded results. To address this issue, this paper introduces KGSR, a kernel-guided network that trains both upscaling and downscaling networks to generate high-quality high-resolution images even without knowing the actual downscaling process.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Yifan Chen, Xuelong Li
Summary: Gait recognition is a popular technology for identification due to its ability to capture gait features over long distances without cooperation. However, current methods face challenges as they use a single network to extract both temporal and spatial features. To solve this problem, we propose a two-branch network that focuses on spatial and temporal feature extraction separately. By combining these features, we can effectively learn the spatio-temporal information of gait sequences.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Wei Shi, Wentao Zhang, Wei-shi Zheng, Ruixuan Wang
Summary: This article proposes a simple yet effective visualization framework called PAMI, which does not require detailed model structure and parameters to obtain visualization results. It can be applied to various prediction tasks with different model backbones and input formats.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Xiaobo Hu, Jianbo Su, Jun Zhang
Summary: This paper reviews the latest technologies in pattern recognition, highlighting their instabilities and failures in practical applications. From a control perspective, the significance of disturbance rejection in pattern recognition is discussed, and the existing problems are summarized. Finally, potential solutions related to the application of compensation on features are discussed to emphasize future research directions.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Andres Felipe Posada-Moreno, Nikita Surya, Sebastian Trimpe
Summary: Convolutional neural networks are widely used in critical systems, and explainable artificial intelligence has proposed methods for generating high-level explanations. However, these methods lack the ability to determine the location of concepts. To address this, we propose a novel method for automatic concept extraction and localization based on pixel-wise aggregations, and validate it using synthetic datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Peng Bao, Jianian Li, Rong Yan, Zhongyi Liu
Summary: In this paper, a novel Dynamic Graph Contrastive Learning framework, DyGCL, is proposed to capture the temporal consistency in dynamic graphs and achieve good performance in node representation learning.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, Olaf Wolkenhauer
Summary: Research indicates that deep generative models perform poorly compared to linear interpolation-based methods for synthetic data generation on small, imbalanced tabular datasets. To address this, a new approach called ConvGeN, combining convex space learning with deep generative models, has been proposed. ConvGeN improves imbalanced classification on small datasets while remaining competitive with existing linear interpolation methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Khondaker Tasrif Noor, Antonio Robles-Kelly
Summary: In this paper, the authors propose H-CapsNet, a capsule network designed for hierarchical image classification. The network effectively captures hierarchical relationships using dedicated capsules for each class hierarchy. A modified hinge loss is utilized to enforce consistency among the involved hierarchies. Additionally, a strategy for dynamically adjusting training parameters is presented to achieve better balance between the class hierarchies. Experimental results demonstrate that H-CapsNet outperforms competing hierarchical classification networks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Lei Liu, Guorun Li, Yuefeng Du, Xiaoyu Li, Xiuheng Wu, Zhi Qiao, Tianyi Wang
Summary: This study proposes a new agricultural image segmentation model called CS-Net, which uses Simple-Attention Block and Simpleformer to improve accuracy and inference speed, and addresses the issue of performance collapse of Transformers in agricultural image processing.
PATTERN RECOGNITION
(2024)