Article
Biology
Behrouz Rostami, D. M. Anisuzzaman, Chuanbo Wang, Sandeep Gopalakrishnan, Jeffrey Niezgoda, Zeyun Yu
Summary: Wound classification plays a crucial role in medical diagnosis, and a high-performance classifier can reduce time and financial costs. This study introduced a deep learning-based classifier with superior classification accuracy, showing potential applications in wound image classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Shengli Wu, Jinlong Li, Weimin Ding
Summary: This paper presents a formal description of a set of properties for majority voting and weighted majority voting in ensemble learning. The performance of each component classifier and the dissimilarity between component classifiers are evaluated using the same metric - Euclidean distance. The results of this paper are useful for understanding the fundamental properties of these combination schemes and ensemble classifiers in general.
Article
Computer Science, Artificial Intelligence
Marcos M. Raimundo, Thalita F. Drumond, Alan Caio R. Marques, Christiano Lyra, Anderson Rocha, Fernando J. Von Zuben
Summary: The research proposes a novel framework to obtain representative and diverse L-2-regularized multinomial models, based on valuable tradeoffs between prediction error and model complexity. NISE method is used for hyperparameter tuning in a multiobjective context, promoting high performance in multiclass classification. The experiments show competitive performance in various setups, taking a broad set of multiclass classification methods as contenders.
Article
Automation & Control Systems
Dejene M. Sime, Guotai Wang, Zhi Zeng, Wei Wang, Bei Peng
Summary: In this article, a novel method for semisupervised defect segmentation based on pairwise similarity map consistency and ensemble-based cross pseudolabels is proposed. It achieved significant performance improvement over the baseline and current state-of-the-art methods, and demonstrated state-of-the-art results on three different datasets.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Siyu Huang, Tianyang Wang, Haoyi Xiong, Bihan Wen, Jun Huan, Dejing Dou
Summary: This study presents a novel deep active learning approach that utilizes temporal output discrepancy to estimate sample loss and select informative unlabeled samples. The method is efficient, flexible, and task-agnostic, demonstrating superior performance in image classification and semantic segmentation tasks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Jian Zhong, Xiangping Zeng, Wenming Cao, Si Wu, Cheng Liu, Zhiwen Yu, Hau-San Wong
Summary: This article proposes a semisupervised multiple choice learning approach to enhance the predictive performance of classification models by jointly training a network ensemble on partially labeled data.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Chemistry, Multidisciplinary
Nabeela Kausar, Abdul Hameed, Mohsin Sattar, Ramiza Ashraf, Ali Shariq Imran, Muhammad Zain ul Abidin, Ammara Ali
Summary: The widespread disease of skin cancer is challenging for dermatologists to diagnose due to the similarity of classes, leading to an accuracy of 62% to 80%. However, utilizing machine learning for classification has shown promise in improving accuracy. Our deep learning-based ensemble models have demonstrated higher accuracy in multiclass skin cancer classification compared to individual deep learners and dermatologists' diagnosis.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Te Zhang, Zhaohong Deng, Hisao Ishibuchi, Lie Meng Pang
Summary: TSK fuzzy systems have been widely applied in supervised learning, but label noise in real-world data can have a negative impact on the learning process. This article introduces a robust algorithm, RTSK-FS-SS, based on semi-supervised learning and intuitionistic fuzzy set method to detect and handle label noise in data.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Automation & Control Systems
Xin Gao, Yang He, Mi Zhang, Xinping Diao, Xiao Jing, Bing Ren, Weijia Ji
Summary: The paper proposes a differential partition sampling ensemble method (DPSE) in the OVA framework, which combines random undersampling and SMOTE to address class imbalance issues and improve classification performance in multiclass tasks.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Agriculture, Multidisciplinary
Prabhjot Kaur, Mukund Pratap Singh, Anand Muni Mishra, Achyut Shankar, Prabhishek Singh, Manoj Diwakar, Soumya Ranjan Nayak
Summary: Tomato plant leaf diseases pose a major risk to plant growth, and detecting and diagnosing these diseases is a complex task for farmers. This study proposes a deep ensemble learning model for autonomous identification of tomato plant diseases. The results show that this model achieves higher accuracy compared to other models.
TURKISH JOURNAL OF AGRICULTURE AND FORESTRY
(2023)
Article
Computer Science, Artificial Intelligence
Binayak Panda, Sudhanshu Shekhar Bisoyi, Sidhanta Panigrahy
Summary: Dependence on the internet and computer programs highlights the significance of computer programs in our daily lives. The increasing demand for computer programs motivates malware developers to create more malware. Researchers face challenges in protecting themselves from potential risks due to the usage of code obfuscation techniques by malware authors. They are interested in using deep learning approaches to analyze the behavior of a wide range of virus variants.
PEERJ COMPUTER SCIENCE
(2023)
Article
Computer Science, Software Engineering
Gabor Szucs
Summary: Two questions in multiclass classification problems, how to combine base classifiers and how to design binary classifiers, were addressed using the proposed Min-Max ECOC method, which minimizes the largest values of error ratios in a deep neural network-based classifier. The theoretical and experimental research showed promising results, with the suggested method outperforming other methods in ensemble learning literature.
Article
Computer Science, Artificial Intelligence
Yue Duan, Zhen Zhao, Lei Qi, Lei Wang, Luping Zhou, Yinghuan Shi, Yang Gao
Summary: In this article, a novel approach called MutexMatch is proposed for semi-supervised learning, which effectively utilizes low-confidence samples through mutex-based consistency regularization. Compared to traditional methods, MutexMatch achieves superior performance on multiple benchmark datasets, even with limited labeled data.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Faisal Shahzad, Abdul Mannan, Abdul Rehman Javed, Ahmad S. Almadhor, Thar Baker, Dhiya Al-Jumeily Obe
Summary: This paper proposes a novel approach, CAD, for cloud-based anomaly detection. CAD utilizes an ensemble machine learning model and a convolutional neural network long short-term memory model to achieve high accuracy on a complex dataset.
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS
(2022)
Article
Chemistry, Analytical
Antonios Vogiatzis, Stavros Orfanoudakis, Georgios Chalkiadakis, Konstantia Moirogiorgou, Michalis Zervakis
Summary: Multiclass image classification is a complex task that can be addressed through decomposition-based strategies. This work focuses on improving the efficiency of these methods and introduces four techniques for optimizing the ensemble phase of multiclass classification. The proposed methods, including a mixture of experts scheme and the combination of Bayes' theorem with arbitrary decompositions, significantly reduce training complexity and improve classification accuracy compared to traditional techniques.
Article
Automation & Control Systems
Guofu Zhang, Zhaopin Su, Miqing Li, Meibin Qi, Jianguo Jiang, Xin Yao
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Ke Li, Renzhi Chen, Guangtao Fu, Xin Yao
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2019)
Article
Computer Science, Information Systems
Zhaopin Su, Guofu Zhang, Feng Yue, Lejie Chang, Jianguo Jiang, Xin Yao
IEEE TRANSACTIONS ON MULTIMEDIA
(2018)
Article
Engineering, Mechanical
He Ma, Ziyang Li, Mohamad Tayarani, Guoxiang Lu, Hongming Xu, Xin Yao
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2019)
Article
Computer Science, Artificial Intelligence
Shuo Wang, Leandro L. Minku, Xin Yao
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2018)
Article
Automation & Control Systems
Zhichen Gong, Huanhuan Chen, Bo Yuan, Xin Yao
IEEE TRANSACTIONS ON CYBERNETICS
(2019)
Article
Engineering, Marine
Yuntao Dai, Ran Cheng, Xin Yao, Liqiang Liu
Article
Computer Science, Artificial Intelligence
Borhan Kazimipour, Mohammad Nabi Omidvar, A. K. Qin, Xiaodong Li, Xin Yao
APPLIED SOFT COMPUTING
(2019)
Editorial Material
Computer Science, Artificial Intelligence
Shuo Wang, Leandro L. Minku, Nitesh Chawla, Xin Yao
Article
Computer Science, Artificial Intelligence
Ke Li, Renzhi Chen, Dragan Savic, Xin Yao
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Wenjing Hong, Ke Tang, Aimin Zhou, Hisao Ishibuchi, Xin Yao
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2019)
Article
Computer Science, Artificial Intelligence
Ran Cheng, Mohammad Nabi Omidvar, Amir H. Gandomi, Bernhard Sendhoff, Stefan Menzel, Xin Yao
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2019)
Article
Computer Science, Artificial Intelligence
Chaoyue Wang, Chang Xu, Xin Yao, Dacheng Tao
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2019)
Article
Computer Science, Artificial Intelligence
Cheng He, Lianghao Li, Ye Tian, Xingyi Zhang, Ran Cheng, Yaochu Jin, Xin Yao
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Chao Qian, Yibo Zhang, Ke Tang, Xin Yao
THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
(2018)