Article
Computer Science, Information Systems
Liyan Jia, Zhiping Wang, Pengfei Sun, Zhaohui Xu, Sibo Yang
Summary: This paper proposes a novel approach based on XGBoost and TDMO to address the issue of data distribution. By training multiple balanced subsets, filtering noise, and combining multiple samples, the diversity of the minority class is expanded, resulting in superior classification results compared to other methods.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Omid Soleiman-garmabaki, Mohammad Hossein Rezvani
Summary: This paper examines the factors affecting customer churn in the telecom industry and analyzes them using various data mining classification methods. By evaluating different criteria, this paper demonstrates the trade-off between speed and accuracy in hybrid classifiers and proposes a more accurate combined classification method.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Dohyun Lee, Kyoungok Kim
Summary: Resampling, especially oversampling, is a widely used approach to handle class imbalance in machine learning. This study proposes a method to determine the oversampling size less than the sample size needed for class balance, based on classification complexity, improving classification performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Sedat Korkmaz, Mehmet Akif Sahman, Ahmet Cevahir Cinar, Ersin Kaya
Summary: The study aims to determine the best oversampling strategy for imbalanced datasets in terms of AUC and G-Mean metrics by using 16 different strategies. Through experiments on 44 datasets, the 6th, 1th, and 3th Debohid strategies show superior performance in AUC and G-Mean metrics when using different classifiers.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Cybernetics
Sara Tavassoli, Hamidreza Koosha
Summary: This paper introduces three novel ensemble classifiers based on bagging and boosting for customer churn prediction. The proposed methods show significant advantages in customer churn prediction, with bagged bagging algorithm demonstrating high accuracy and precision results.
Article
Computer Science, Artificial Intelligence
Gaoshan Wang, Jian Wang, Kejing He
Summary: This paper proposes a hybrid strategy called Majority-to-Minority Resampling (MMR) to solve the problem of class imbalance by adaptively sampling potential instances from the majority class to augment the minority class. To reduce the loss of information after sampling, a Majority-to-Minority Boosting (MMBoost) algorithm is also proposed to dynamically adjust the weights of the sampled instances for classification. Extensive experiments using real-world datasets demonstrate that the proposed framework achieves competitive performance in dealing with imbalanced data.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Bing Zhu, Xin Pan, Seppe vanden Broucke, Jin Xiao
Summary: Class imbalance is a critical issue in customer classification, and various techniques have been proposed to address this problem. This study introduces a novel GAN-based hybrid sampling method that effectively tackles class imbalance and demonstrates superior performance in experiments.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Jiuxiang Song, Jizhong Liu
Summary: This study proposes a multi-fidelity model called MFSMOTE to improve the resolution of class imbalance problems in classification models. MFSMOTE divides the data into low-fidelity and high-fidelity groups, and utilizes prior information to train the classification model, showing promising performance.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Business
Theresa Gattermann-Itschert, Ulrich W. Thonemann
Summary: Customer churn prediction is crucial for companies to retain at-risk customers through proactive measures. This study develops a churn prediction model for a non-contractual B2B wholesale setting and demonstrates that contacting customers with the highest predicted churn probabilities significantly reduces churn and has a positive financial impact on revenue development. The study also identifies important features, including recency of contact with a field representative, in addition to common features such as recency, frequency, and monetary value.
INDUSTRIAL MARKETING MANAGEMENT
(2022)
Article
Computer Science, Artificial Intelligence
Sahar Hassanzadeh Mostafaei, Jafar Tanha
Summary: Most real-world datasets are imbalanced, which leads to biased classifiers favoring the majority class. This paper proposes a new under-sampling technique called Peak clustering and a boosting-based algorithm named OUBoost, which combines Peak under-sampling with SMOTE over-sampling. OUBoost selects useful examples from the majority class and creates synthetic examples for the minority class. Experimental results on 30 imbalanced datasets demonstrate the improved prediction performance in the minority class using OUBoost. Time comparisons and statistical tests further analyze the proposed algorithm.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Roozbeh Razavi-Far, Maryam Farajzadeh-Zanajni, Boyu Wang, Mehrdad Saif, Shiladitya Chakrabarti
Summary: The correct classification of rare samples is crucial and this article proposes novel oversampling strategies based on imputation methods to address this issue. The techniques are designed to generate synthetic minority class samples and outperform other methods according to performance metrics such as AUC, F-measure, and G-mean.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Hardware & Architecture
Jinyan Li, Yaoyang Wu, Simon Fong, Raymond K. Wong, Victor W. Chu, Kok-leong Ong, Kelvin K. L. Wong
Summary: Process mining is increasingly crucial in workflow model reconstructions, and the efficacy of this method relies on data mining algorithms accurately classifying future events from process logs. Our proposed methods address class imbalance by integrating swarm intelligence algorithms, aiming to enhance classification accuracy and confidence levels in process mining.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Szilvia Szeghalmy, Attila Fazekas
Summary: In this paper, a new oversampling method is proposed and optimized to adapt to different datasets. Experimental results demonstrate its superior performance compared to other well-known samplers on various classifiers.
Article
Chemistry, Multidisciplinary
Meng Han, Ang Li, Zhihui Gao, Dongliang Mu, Shujuan Liu
Summary: In order to address the imbalance and concept drift problems in multi-class data streams, a Hybrid Sampling and Dynamic Weighted-based classification method for Multi-class Imbalanced data stream (HSDW-MI) is proposed. The HSDW-MI algorithm tackles the imbalance and concept drift problems through hybrid sampling and dynamic weighting phases respectively. Experimental results show that HSDW-MI outperforms other algorithms in terms of classification capabilities and consistency.
APPLIED SCIENCES-BASEL
(2023)
Article
Energy & Fuels
Guorui Ren, Jie Wan, Yanjia Wang, Kun Yao, Junfeng Fu, Jilai Yu
Summary: Predicting wind power ramp events based on historical time series has gained attention recently. However, the class imbalance problem affects the prediction accuracy. This study proposes a novel method called LOS considering wind power amplitudes and occurrence frequency. A hybrid sampling method, EB-LOS, is also proposed. The results show that EB-LOS achieves the best prediction performance using the BPNN and LSTM models.
ENERGY SCIENCE & ENGINEERING
(2023)
Article
Operations Research & Management Science
Matthias Bogaert, Michel Ballings, Dirk Van den Poel
ANNALS OF OPERATIONS RESEARCH
(2018)
Article
Computer Science, Interdisciplinary Applications
Bram Steurtewagen, Dirk Van den Poel
COMPUTERS & CHEMICAL ENGINEERING
(2020)
Article
Management
Matthias Bogaert, Justine Lootens, Dirk Van den Poel, Michel Ballings
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2019)
Article
Agriculture, Multidisciplinary
Arno Liseune, Matthieu Salamone, Dirk Van den Poel, Bonifacius Van Ranst, Miel Hostens
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2020)
Article
Computer Science, Theory & Methods
Giselle van Dongen, Dirk Van den Poel
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2020)
Article
Agriculture, Multidisciplinary
Arno Liseune, Matthieu Salamone, Dirk Van den Poel, Bonifacius van Ranst, Miel Hostens
Summary: This study introduces a deep learning model that can predict the entire lactation curve of dairy cows, outperforming baseline models and improving predictions during the first 26 days of lactation. The framework allows farmers to enhance total production forecast and optimal herd management, and can assist in detecting diseases early and improving animal monitoring systems. By incorporating health, reproduction events, and herd management, the model enables more accurate estimation of future earnings and costs.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Computer Science, Artificial Intelligence
Lisa Schetgen, Matthias Bogaert, Dirk Van den Poel
Summary: This study demonstrates the value of Facebook data in predicting first-time donation behavior and acquiring new donors for nonprofit organizations. The combination of singular value decomposition and logistic regression outperformed other analytical methodologies, with Facebook pages and categories being the most important data types. Factors related to age, education, residence, and other dimensions played a significant role in predicting donation behavior.
DECISION SUPPORT SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Bram Steurtewagen, Dirk Van den Poel
Summary: This study utilizes a supervised machine learning approach, combining sensor and report data, to achieve prediction and diagnosis of equipment failures, highlighting the importance of diagnosis. The combination of statistical methods with proper data treatment can greatly enhance the diagnostic value of machine learning approaches.
COMPUTERS & CHEMICAL ENGINEERING
(2021)
Article
Computer Science, Information Systems
Giselle Van Dongen, Dirk van den Poel
Summary: This study evaluates the scalability of stream processing jobs in four popular frameworks and finds that scaling efficiency is influenced by factors such as cluster layout, scaling direction, framework design, and data characteristics. Recommendations are provided on how to scale clusters effectively.
Article
Computer Science, Information Systems
Giselle van Dongen, Dirk Van den Poel
Summary: This study delves into the critical feature of built-in fault tolerance of four leading frameworks and tests their performance in various fault scenarios. Results show the significant impact of framework design on fault recovery speed.
Article
Management
Matthias Bogaert, Michel Ballings, Rob Bergmans, Dirk Van den Poel
Summary: This study evaluates the feasibility of predicting whether a Facebook user has watched a certain movie genre and builds predictive models and evaluates variable importance. The results show that the adaptive boosting algorithm outperforms others, with time- and frequency-based variables related to media consumption being the most important.
Article
Business
Matthijs Meire, Kelly Hewett, Michel Ballings, V. Kumar, Dirk Van den Poel
JOURNAL OF MARKETING
(2019)
Proceedings Paper
Computer Science, Information Systems
Giselle van Dongen, Bram Steurtewagen, Dirk Van den Poel
2018 IEEE INTERNATIONAL CONGRESS ON BIG DATA (IEEE BIGDATA CONGRESS)
(2018)
Article
Management
Vijay Viswanathan, Edward C. Malthouse, Ewa Maslowska, Steven Hoornaert, Dirk Van den Poel
JOURNAL OF SERVICE MANAGEMENT
(2018)
Meeting Abstract
Agriculture, Dairy & Animal Science
A. Liseune, D. Van den Poel, B. Van Ranst, M. Hostens
JOURNAL OF DAIRY SCIENCE
(2019)
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)