Article
Biology
Miguel Romero, Felipe Kenji Nakano, Jorge Finke, Camilo Rocha, Celine Vens
Summary: With the development of new sequencing technologies, genomic data availability has increased rapidly. Previous studies have used this data to associate genes with biological functions, but often ignored the sparsity and noise in the datasets. This research proposes a method for detecting missing annotations in a hierarchical multi-label classification context, using function relations represented as a hierarchy. Experimental results on rice datasets demonstrate the accuracy and superiority of this method compared to current state-of-the-art approaches.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Chemistry, Analytical
Jingdong Diao, Qingrui Zhou, Hui Wang, Ying Yang
Summary: This paper proposes a hierarchical multi-level class label for multi-class multi-target tracking to synthetically measure the state errors. By introducing a hierarchical tree-structured categorization, a Wasserstein distance type metric can be defined among the distributions. The advantages of this approach are illustrated through examples.
Article
Engineering, Multidisciplinary
Jianwei Liu, Yun Teng, Bo Shi, Xuefeng Ni, Weichu Xiao, Chao Wang, Hongli Liu
Summary: A hierarchical learning approach is proposed to address the imbalanced fastener sample detection issue on railways. By utilizing fastener localization, region classification, and decision tree analysis, the approach achieves high precision and recall rates in detecting fasteners.
Article
Multidisciplinary Sciences
Jiawen Deng, Fuji Ren
Summary: This paper proposes a hierarchical model with label embedding for contextual emotion recognition. By training the label embedding matrix through joint learning, it enhances the final prediction performance.
Article
Computer Science, Artificial Intelligence
Yinglong Ma, Xiaofeng Liu, Lijiao Zhao, Yue Liang, Peng Zhang, Beihong Jin
Summary: This paper introduces a hierarchical multi-label text classification method based on hybrid embedding, combining graph embedding and word embedding; using a level-by-level HMTC approach and conducting extensive experiments on five large-scale real-world datasets, the results show that the method is competitive in classification accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Qing Ye, Changhua Liu
Summary: This paper proposes an intelligent simultaneous fault diagnosis model based on a hierarchical multi-label classification strategy and sparse Bayesian extreme learning machine. The model compares the similarity between an unknown sample and each single fault mode, then outputs the probability of each fault mode occurring. The model performance is evaluated using actual vibration signals and compared with several classical models, showing that the proposed model is more accurate, efficient, and stable.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Huanze Zeng, Argon Chen
Summary: A simple multi-layer classifier (MLC) model with binary split is proposed in the study, which has been thoroughly tested with 40 datasets, showing that binary MLC models are easier to interpret and achieve significantly better performance compared to other models.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Xinyi Zhang, Jiahao Xu, Charlie Soh, Lihui Chen
Summary: Hierarchical multi-label text classification (HMTC) has gained popularity in real-world applications. The proposed LA-HCN model outperforms other state-of-the-art neural network algorithms in HMTC, providing explainability by visualizing learned attention and extracting meaningful information corresponding to different labels.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Geochemistry & Geophysics
Jingzhou Chen, Yuntao Qian
Summary: Hierarchical multilabel classification (HMC) assigns multiple labels to each instance with hierarchical relations. A novel deep network with two output channels and associated loss functions is proposed to learn an HMC classifier for ship classification in remote sensing images, outperforming existing approaches in experiments.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Information Systems
Md Nasim Adnan, Ryan H. L. Ip, Michael Bewong, Md Zahidul Islam
Summary: The proposed decision forest algorithm in this paper achieves better balance through effective synchronization of diversity from different sources, leading to significant improvement in accuracy according to empirical evaluations. It is also competitive in terms of complexity and other relevant parameters.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Brendan Kolisnik, Isaac Hogan, Farhana Zulkernine
Summary: We propose a hierarchical image classification model, Condition-CNN, which improves prediction accuracy and reduces training time by using the Teacher Forcing training algorithm and conditional probabilities. The validation results show that Condition-CNN achieves higher prediction accuracy for Level 1, 2, and 3 classes compared to other baseline CNN models.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Wei Huang, Enhong Chen, Qi Liu, Hui Xiong, Zhenya Huang, Shiwei Tong, Dan Zhang
Summary: Hierarchical multi-label classification (HMC) assigns entities to multiple classes in a taxonomic structure. Previous studies often ignore class dependencies within the hierarchy, leading to incoherent predictions. HmcNet introduces explicit and implicit class hierarchy constraints to generate coherent predictions through a hierarchical attention-based memory unit and a prune-based coherent prediction strategy.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Software Engineering
Yuntao Liu, Yong Dou, Ruochun Jin, Rongchun Li, Peng Qiao
Summary: This paper introduces a hierarchical learning algorithm based on the Bayesian Neural Network classifier with backtracking for large-scale image classification. By establishing a Visual Confusion Label Tree, the algorithm improves the classification ability of the tree classifier by focusing on confusion subsets and utilizing uncertainty information for re-correction. Experiment results show significant improvement over state-of-the-art classifiers, validating the effectiveness of the proposed algorithm.
Article
Mathematics
Xinchun Liu
Summary: This paper uses decision tree and random forest machine learning algorithms to detect financial data of listed companies, and through empirical research, constructs a comprehensive application model with an accuracy of up to 96.58% for identifying financial statement fraud, providing an accurate and practical method for capital market participants.
JOURNAL OF MATHEMATICS
(2021)
Article
Acoustics
Fei Zhao, Zhen Wu, Liang He, Xin-Yu Dai
Summary: This study proposes a hierarchical capsule network for hierarchical text classification, which can distinguish similar categories and solve the problem of error propagation. Experimental results show that our model achieves competitive performance on two widely used datasets.
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
(2023)
Article
Engineering, Manufacturing
Xinghao Yan, Hui Zhao, Kwei Tang
PRODUCTION AND OPERATIONS MANAGEMENT
(2015)
Article
Computer Science, Artificial Intelligence
Kwei Tang, Yen-Liang Chen, Hsiao-Wei Hu
DECISION SUPPORT SYSTEMS
(2008)
Article
Business
Yen-Liang Chen, Mi-Hao Kuo, Shin-Yi Wu, Kwei Tang
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
(2009)
Article
Business
Yen-Liang Chen, Kwei Tang, Chia-Chi Wu, Ru-Yun Jheng
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
(2014)
Article
Management
Hakan Tarakci, Kwei Tang, Sunantha Teyarachakul
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2009)
Article
Management
Zhefang Zhou, Yanjun Li, Kwei Tang
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2009)
Article
Business
Fu-Shiang Tseng, Kwei Tang, Herbert Moskowitz, Robert Plante
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2009)
Article
Computer Science, Artificial Intelligence
Hsiao-Wei Hu, Yen-Liang Chen, Kwei Tang
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2009)
Article
Engineering, Industrial
Hakan Tarakci, Kwei Tang, Sunantha Teyarachakul
Article
Computer Science, Information Systems
Yen-Liang Chen, Chia-Chi Wu, Kwei Tang
INFORMATION SCIENCES
(2009)
Article
Computer Science, Information Systems
Ya-Han Hu, Yen-Liang Chen, Kwei Tang
JOURNAL OF INFORMATION SCIENCE
(2009)
Article
Operations Research & Management Science
Ping H. Huang, Kwei Tang
OPERATIONS RESEARCH LETTERS
(2012)
Article
Automation & Control Systems
Hsiao-Wei Hu, Yen-Liang Chen, Kwei Tang
IEEE TRANSACTIONS ON CYBERNETICS
(2013)
Article
Computer Science, Interdisciplinary Applications
Hung-Pin Kao, Kwei Tang
INFORMS JOURNAL ON COMPUTING
(2014)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)