Article
Computer Science, Artificial Intelligence
Yao Zou, Changchun Gao
Summary: Credit scoring is an effective tool for banks and lending companies to manage potential credit risk, with machine learning algorithms making progress. Ensemble methods like RF and GBDT have become mainstream for precise credit scoring. Combining Bagging and Boosting methods, a supervised augmented GBDT based on extreme learning machine is proposed to significantly improve credit scoring.
Article
Computer Science, Artificial Intelligence
Yao Zou, Changchun Gao, Meng Xia, Congyuan Pang
Summary: Establishing precise credit scoring models is crucial for credit risk management. This study proposes a hybrid ensemble method that combines the benefits of Bagging and boosting ensemble approaches, providing a good solution to balance the trade-off between variance and bias optimization.
INTELLIGENT DATA ANALYSIS
(2022)
Article
Computer Science, Artificial Intelligence
Xiayu Liang, Ying Gao, Shanrong Xu
Summary: In many binary classification tasks, the minority class only accounts for a small part of all instances, leading to imbalance ratio in the datasets. To solve this problem, this paper proposes an anomaly scoring-based ensemble learning framework ASE, which guides the resampling strategy and improves the performance of base classifiers.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Alexander Tornede, Lukas Gehring, Tanja Tornede, Marcel Wever, Eyke Huellermeier
Summary: This paper introduces the problem of meta algorithm selection and presents a general methodological framework and several concrete learning methods. Experimental results show that ensembles of algorithm selectors can significantly outperform single algorithm selectors and have the potential to become the new state of the art in algorithm selection.
Article
Management
Miaojun Bai, Yan Zheng, Yun Shen
Summary: This article introduces the use of gradient boosting survival tree (GBST) model for credit scoring in consumer finance, showing evidence that the model outperforms existing survival models and can significantly reduce overall error.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2022)
Article
Computer Science, Artificial Intelligence
Mahsan Abdoli, Mohammad Akbari, Jamal Shahrabi
Summary: This paper proposes a Bagging Supervised Autoencoder Classifier (BSAC) to address the challenges in credit scoring. The method combines supervised autoencoders, representation learning, and the Bagging mechanism to improve the prediction of loan application outcomes.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Metallurgy & Metallurgical Engineering
Wang Shi-ming, Zhou Jian, Li Chuan-qi, Danial Jahed Armaghani, Li Xi-bing, Hani S. Mitri
Summary: Rockburst prediction in hard rock mines was examined using three tree-based ensemble methods, with the dataset evaluated using six widely accepted indices. The study found that bagging algorithm performed best in predicting the potential of rockburst compared to other algorithms and empirical criteria methods.
JOURNAL OF CENTRAL SOUTH UNIVERSITY
(2021)
Article
Management
Hao Li, Hao Qiu, Shu Sun, Jun Chang, Wenting Tu
Summary: The article introduces a new approach called OCDDEL for training credit scoring models, which relies on past accepted applications and their true labels for better accuracy compared to traditional methods and previous techniques.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2022)
Article
Computer Science, Artificial Intelligence
Yilun Jin, Yanan Liu, Wenyu Zhang, Shuai Zhang, Yu Lou
Summary: This study proposes a novel multi-stage ensemble model for credit scoring, utilizing multiple K-means-based selective undersampling methods. Experimental results demonstrate the superiority of the proposed model in performance over other benchmark models.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Information Systems
Ahmed A. Khalil, Zaiming Liu, Ahmad Salah, Ahmed Fathalla, Ahmed Ali
Summary: Insolvency is a crucial problem for insurance companies, and this study explores the prediction of insurance company insolvency using ensemble learning methods in the Egyptian market. A dataset of 11 Egyptian insurance companies was collected, and different evaluation metrics were used to assess the proposed models.
Article
Acoustics
S. V. V. S. Narayana Pichika, Godhala Meganaa, Sabareesh Geetha Rajasekharan, Aruna Malapati
Summary: Condition monitoring is used to diagnose and predict gearbox failures in wind turbines. A hybrid ensemble method combining Boosting, Bagging, and stacking techniques is proposed to improve fault classification performance. Experimental results show that the developed method achieves a high accuracy of 92%.
Article
Thermodynamics
Rahul Gupta, Anil Kumar Yadav, S. K. Jha, Pawan Kumar Pathak
Summary: This article proposes a feature selection method based on VIF-MI and an improved ensemble method for predicting solar irradiance. The results show that the proposed method outperforms other models in estimation performance and error reduction.
INTERNATIONAL JOURNAL OF GREEN ENERGY
(2023)
Article
Business, Finance
Xiaowei Chen, Cong Zhai
Summary: This study compares the performance of five ensemble learning models based on bagging and boosting in detecting financial fraud in the financial field. The analysis was conducted using data from Chinese A-share listed companies from 2012 to 2022, including the COVID-19 pandemic period. The results show that bagging outperforms boosting in various evaluation indicators, with profitability and asset quality positively affecting financial fraud. This study reveals the mechanism by which ensemble learning affects financial fraud detection and expands related research in the financial field.
ACCOUNTING AND FINANCE
(2023)
Article
Computer Science, Artificial Intelligence
Manuel Menor-Flores, Miguel A. Vega-Rodriguez
Summary: The number of investigations attempting to align protein-protein interaction (PPI) networks has increased with the growth of studies focused on collecting PPI data. However, there is no standard approach to align PPI networks, and global aligners encounter difficulties in constructing alignments with high biological and structural quality. To address this issue, we propose an innovative ensemble technique that combines the strengths of aligners in the PPI network alignment field while avoiding their weaknesses. Our approach achieves alignments of higher quality and requires minimal time compared to individual aligners.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Alex Wozniakowski, Jayne Thompson, Mile Gu, Felix C. Binder
Summary: The paper presents a reformulation of the standard formulation of gradient boosting algorithm, allowing it to improve nonconstant models and introducing a variant of multi-target stacking. Experimental results demonstrate that the approach outperforms state-of-the-art calibration models even with limited training examples, and significantly surpasses LightGBM and a data-driven reimplementation of the calibration model.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2021)
Article
Business
Wei Du, Qiang Yan, Wenping Zhang, Jian Ma
Summary: This study introduces an interpretable knowledge-aware patent recommendation model (IKPRM) for patent trading, which outperforms baseline methods in hit ratio and nDCG. The model leverages a patent knowledge graph (PKG) and paths to achieve recommendation interpretability, demonstrating good performance and transparency in explaining candidate patent recommendations.
Article
Computer Science, Information Systems
Wei Du, Guanran Jiang, Wei Xu, Jian Ma
Summary: This paper proposes a knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM) method for patent trading recommendation, which successfully captures the sequential pattern in a company's historical records using knowledge graph embeddings and bidirectional long short-term memory network. An attention mechanism is designed to address the diverse technology interests of a company. Experimental results demonstrate the superior performance of KBiLSTM compared to other baselines in terms of F1 and normalized discounted cumulative gain (nDCG) for patent trading recommendation.
JOURNAL OF INFORMATION SCIENCE
(2023)
Article
Automation & Control Systems
Gang Wang, Xinyue Zhang, Hanru Wang, Yan Chu, Zhen Shao
Summary: The establishment of academic groups on scientific social network has provided new opportunities for collaboration among researchers. Existing methods of conducting paper recommendation to these groups often fail to fully utilize the abundant group information, which can affect the accuracy of recommendations.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Gang Wang, Hegong Zhu, Zhangjun Wu, Min Yang
Summary: This paper proposes a novel random subspace method (RS-SBL) for soft sensors, which extracts features through deep learning and improves the prediction performance through a fusion strategy.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2022)
Article
Engineering, Multidisciplinary
Renzhi Gao, Hegong Zhu, Gang Wang, Zhangjun Wu
Summary: This paper proposes a novel denoising and multiscale residual deep network (DMRDN) for soft sensor modeling, which can effectively handle the noise and nonstationary conditions in industrial processes, and improve the learning capacity and measurement accuracy of deep learning methods.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Gang Wang, Hui Li, Feng Zhang, Zhangjun Wu
Summary: This paper proposes a Feature Fusion based Ensemble Method (FFEM) for predicting the Remaining Useful Life (RUL) of machinery. The method utilizes the characteristics of signal analysis features and deep representation features, and combines different types of features using a fusion method. Experimental results on a run-to-failure dataset of bearings demonstrate the effectiveness of the proposed method.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Multidisciplinary
Xiaoyu Yao, Hegong Zhu, Gang Wang, Zhangjun Wu, Wei Chu
Summary: A triple attention-based deep convolutional recurrent network (TADCRN) is proposed in this paper to handle the soft sensor modeling issues in industrial processes. The method utilizes multiscale 1d-CNN, space-wise attention, and time-wise attention to extract critical features and capture dependencies among data samples. Experimental results show that the proposed method outperforms conventional machine learning and deep learning methods.
Article
Computer Science, Information Systems
Gang Wang, Hanru Wang, Jing Liu, Ying Yang
Summary: This paper proposes a recommendation method that leverages fine-grained user preferences and graph neural networks to address the issues of ignoring personalized reasons and handling complex data in traditional recommendation methods. Experiments show that the proposed method outperforms baselines in terms of precision and recall.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Gang Wang, Yanan Zhang, Mingfeng Lu, Zhangjun Wu
Summary: A hierarchical graph neural network with adaptive cross-graph fusion (HGNN-ACGF) method is proposed to improve the remaining useful life (RUL) prediction performance by fully leveraging structural information.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Energy & Fuels
Xiuna Zhou, Junfeng Dong, Gang Wang, Yan Chu
Summary: This study establishes a model of residential electricity packages that considers load difference penalty during peak and off-peak seasons. A mathematical model focusing on the minimization of load difference is also established. Through simulation results, it is demonstrated that the profit of retailer increases by 37.35% and load difference during peak and off-peak seasons decreases by 78.01%.
FRONTIERS IN ENERGY RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Gang Wang, Jingling Ma, Ying Wang, Tao Tao, Gang Ren, Hegong Zhu
Summary: Supervised and unsupervised deep representation features have been studied separately in foreign exchange rate prediction (FERP). However, the complementarity of these features has not been explored. This paper proposes a novel method, SUDF-RS, that considers both supervised and unsupervised deep representation features for FERP. Experimental results show that SUDF-RS outperforms benchmark methods in terms of both MAPE and RMSE.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Multidisciplinary
Gang Wang, Yanan Zhang, Feng Zhang, Zhangjun Wu
Summary: Fault diagnosis is important for intelligent manufacturing to improve efficiency and reduce machine breakdown risk. Previous research has used shallow statistical features and deep representation features to describe fault information. However, combining these features under class-imbalance situation is a challenge. To address this issue, an Imbalanced Ensemble Method with DenseNet and Evidential Reasoning Rule (IEMD-ER) is proposed for machinery fault diagnosis.
Article
Automation & Control Systems
Gang Wang, Feng Zhang, Zhaojian Li
Summary: Feature selection plays a critical role in data-driven fault diagnosis, but existing methods often neglect the inherent properties and cross-feature correlations. This article proposes a multiview feature selection method, MFSICC, which incorporates complementary and consensus properties among the multiview features based on a structured sparsity learning model. An iterative algorithm is also proposed to solve the MFSICC problem with approved convergence. Experiments on real-world datasets validate the effectiveness of MFSICC in bearing and gearbox fault diagnosis.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Business
Weiwei Deng, Jian Ma
Summary: A knowledge graph approach is proposed to address the lack of a method that matches patents with company needs in patent transfer. Experimental results show that the proposed method outperforms several baseline methods in terms of precision, recall, F-score, and mean average precision.
ELECTRONIC COMMERCE RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Gang Wang, Feng Zhang
Summary: A new deep learning model named SMAML is proposed for tool wear monitoring, utilizing an encoder and decoder to achieve monitoring and prediction of tool wear, with experimental results demonstrating its effectiveness.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
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)
