Article
Computer Science, Artificial Intelligence
Zhong-Liang Zhang, Rui-Rui Peng, Yuan-Peng Ruan, Jian Wu, Xing-Gang Luo
Summary: This paper proposes a method for addressing the class imbalance learning problem by using an overproduce-and-choose strategy to generate synthetic examples. Experimental results demonstrate that the proposed method outperforms SMOTE and its variants in terms of metrics such as G-mean and area under the curve.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Javad Elmi, Mahdi Eftekhari
Summary: This paper introduces a novel approach of combining dynamic selection methods via multi-layer selectors, optimizing the evaluation and selection process of classifiers' competence levels. Experimental results show that the proposed framework improves classification accuracy compared to the current state-of-the-art dynamic ensemble selection techniques.
APPLIED SOFT COMPUTING
(2021)
Article
Psychology, Experimental
Angus Reynolds, Roderick Garton, Peter Kvam, James Sauer, Adam F. Osth, Andrew Heathcote
Summary: The study proposes a dynamic theory of decisions not to choose the correct option, highlighting the importance of not-know judgments in various domains. Experimental results show that high similarity increases the probability of not-know responses. Individual differences in the use of not-know responses were also identified in the study.
JOURNAL OF EXPERIMENTAL PSYCHOLOGY-GENERAL
(2021)
Article
Multidisciplinary Sciences
Kobe Desender, Luc Vermeylen, Tom Verguts
Summary: The authors find that current measures of metacognition are confounded with response caution and propose an alternative dynamic measure. They show a relationship between response caution and the popular measure of metacognition, M-ratio. Additionally, they demonstrate that using a dynamic measure, v-ratio, can avoid the impact of the speed-accuracy tradeoff in metacognition assessment.
NATURE COMMUNICATIONS
(2022)
Article
Automation & Control Systems
Zhenkun Wang, Qingfu Zhang, Yew-Soon Ong, Shunyu Yao, Haitao Liu, Jianping Luo
Summary: In this paper, we propose an adaptive subproblem selection strategy and a new acquisition function to improve efficiency and balance between exploitation and exploration in dealing with the expensive multiobjective optimization problem.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Liang Chen, Hanyang Wang, Darong Pan, Hao Wang, Wenyan Gan, Duodian Wang, Tao Zhu
Summary: In this paper, a dynamic multiobjective evolutionary algorithm (DMOEA) with an adaptive response mechanism selection strategy is proposed. The proposed algorithm combines an adaptive response mechanism selection (ARMS) strategy and a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It is tested on two groups of test instances and compared with other algorithms, and the results show its effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Shiyuan Fu, Xin Gao, Baofeng Li, Bing Xue, Xin Jia, Zijian Huang, Guangyao Zhang, Xu Huang
Summary: This paper proposes two outlier-sensitive measures to estimate the competence of base classifiers for semi-supervised dynamic ensemble anomaly detection models. Experimental results show that dynamic ensemble models with these competence measures outperform other typical ensemble models in performance.
NEURAL PROCESSING LETTERS
(2023)
Article
Biology
Jinggang Zhang, Peter Santema, Zixuan Lin, Lixing Yang, Meijun Liu, Jianqiang Li, Wenhong Deng, Bart Kempenaers
Summary: The arms race between brood parasites and their hosts provides a classic model to study coevolution. Hosts often reject the parasitic egg, and brood parasites should therefore select host nests in which the colour of the eggs best matches that of their own. We reported on a study of Daurian redstarts, which show a distinct egg-colour dimorphism, with females laying either blue or pink eggs. The study demonstrated that cuckoos actively choose redstart nests in which the egg colour matches the colour of their own eggs, providing direct experimental evidence in support of the egg matching hypothesis.
PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
(2023)
Article
Engineering, Chemical
Zhihua Zhang, Jinfeng Bai, Shaojun Li, Yang Liu, Chao Li, Xiangyun Zhong, Yang Geng
Summary: This paper proposes a dynamic model management strategy based on adaptive surrogate selection to assist in solving optimization problems in coal gasification process. By selecting appropriate surrogate models, introducing local models, and considering unexplored samples, the optimization performance of the particle swarm optimization algorithm and the effective syngas yield of the coal gasification process can be significantly improved.
JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS
(2022)
Article
Computer Science, Artificial Intelligence
Ping Yuan, Biao Wang, Zhizhong Mao
Summary: This study proposed a dynamic outlier ensemble method to relax the assumption of independent errors made by base detectors. Artificial outliers are generated using the concept of multiple classifier behavior to estimate competences, and validation sets are optimized to find more representative objects. Competences of base detectors are estimated using a probabilistic method, and a switching mechanism is proposed for robust detection results.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Jinhua Zheng, Yubing Zhou, Juan Zou, Shengxiang Yang, Junwei Ou, Yaru Hu
Summary: In this paper, a hybrid prediction strategy based on the classification of decision variables is proposed to track moving optima in dynamic multi-objective optimization problems. By analyzing the impact and using different prediction methods for decision variables in a new environment, the algorithm can significantly improve dynamic optimization performance according to experimental results.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Information Systems
Faten Khalid Karim, Hela Elmannai, Abdelrahman Seleem, Safwat Hamad, Samih M. Mostafa
Summary: Handling missing values and feature selection are crucial preprocessing tasks for pattern recognition and machine learning applications. This paper proposes a new algorithm called CBRSL, which effectively manipulates missing values using feature selection. Experimental results demonstrate that CBRSL outperforms other imputation methods in terms of accuracy and can handle missing values generated from various mechanisms.
Article
Computer Science, Information Systems
Xiaoyan Zhu, Jiaxuan Li, Jingtao Ren, Jiayin Wang, Guangtao Wang
Summary: This study proposes a new method called MLDE for solving the multi-label classification problem. It selects the most competent ensemble of base classifiers to predict each unseen instance, effectively utilizing label correlation and achieving better performance.
INFORMATION SCIENCES
(2023)
Article
Mathematics
Carine M. Rebello, Marcio A. F. Martins, Jose M. Loureiro, Alirio E. Rodrigues, Ana M. Ribeiro, Idelfonso B. R. Nogueira
Summary: This study proposed a new methodology to extend the optimal point to an optimal region by drawing confidence regions of all minima found by the optimization algorithm, and successfully applied it in a case study of chemical engineering.
Article
Computer Science, Artificial Intelligence
Felipe N. Walmsley, George D. C. Cavalcanti, Robert Sabourin, Rafael M. O. Cruz
Summary: In the literature on classification problems, the impacts of label noise on performance is widely discussed, however current methods are not always effective in combating noise. This study investigates the effects of noise on dynamic selection algorithms, proposing the use of Multiple-Set Dynamic Selection method to supplant the ENN algorithm, and finds that the K-Nearest Oracles-Union algorithm is the only method unaffected by noise.
INFORMATION FUSION
(2022)
Article
Computer Science, Artificial Intelligence
Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
NEURAL COMPUTING & APPLICATIONS
(2018)
Article
Computer Science, Artificial Intelligence
Anandarup Roy, Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
Article
Computer Science, Artificial Intelligence
M. Ali Akber Dewan, E. Granger, G. -L. Marcialis, R. Sabourin, F. Roli
PATTERN RECOGNITION
(2016)
Article
Computer Science, Artificial Intelligence
Simon Bernard, Clement Chatelain, Sebastien Adam, Robert Sabourin
PATTERN RECOGNITION
(2016)
Article
Computer Science, Artificial Intelligence
Saman Bashbaghi, Eric Granger, Robert Sabourin, Guillaume-Alexandre Bilodeau
MACHINE VISION AND APPLICATIONS
(2017)
Article
Computer Science, Artificial Intelligence
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
PATTERN RECOGNITION
(2017)
Article
Computer Science, Artificial Intelligence
Saman Bashbaghi, Eric Granger, Robert Sabourin, Guillaume-Alexandre Bilodeau
PATTERN RECOGNITION
(2017)
Article
Computer Science, Artificial Intelligence
Dayvid V. R. Oliveira, George D. C. Cavalcanti, Robert Sabourin
PATTERN RECOGNITION
(2017)
Article
Computer Science, Artificial Intelligence
Luiz G. Hafemann, Luiz S. Oliveira, Robert Sabourin
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION
(2018)
Article
Computer Science, Artificial Intelligence
Andre L. Brun, Alceu S. Britto, Luiz S. Oliveira, Fabricio Enernbreck, Robert Sabourin
PATTERN RECOGNITION
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Marcelo T. Pereira, Alceu S. Britto, Luiz S. Oliveira, Robert Sabourin
2018 IEEE 30TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Eunelson J. Silva, Alceu S. Britto, Luiz. S. Oliveira, Fabricio Enembreck, Robert Sabourin, Alessandro L. Koerich
2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Paulo R. Lisboa de Almeida, Luiz S. Oliveira, Alceu de Souza Britto, Robert Sabourin
2016 IEEE 28TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2016)
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
M. Ali Akber Dewan, E. Granger, R. Sabourin, G. -L. Marcialis, F. Roli
2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI)
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
Diego Bertolini, Luiz S. Oliveira, Robert Sabourin
PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2015
(2015)
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)