Article
Computer Science, Artificial Intelligence
Yange Sun, Honghua Dai
Summary: Ensemble learning is used to tackle concept drift in big evolving data streams. A Pareto-based multi-objective optimization technique is introduced in this paper to learn high-performance base classifiers, leading to the proposal of a multi-objective evolutionary ensemble learning scheme named PAD. The approach aims to enhance ensemble generalization in an evolving data stream environment by balancing accuracy and diversity, and includes an adaptive window change detection mechanism for tracking different drifts.
NEURAL COMPUTING & APPLICATIONS
(2021)
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
Computer Science, Artificial Intelligence
Kanu Goel, Shalini Batra
Summary: The paper introduces a novel concept drift handling approach named TLP-EnAbLe, which maintains suitable learners for current concept by adding diversity-based pruning to traditional accuracy-based pruning. This approach effectively handles concept drift by deferring similarity-based pruning and monitoring the performance of learners in real time.
Article
Computer Science, Artificial Intelligence
Botao Jiao, Yinan Guo, Dunwei Gong, Qiuju Chen
Summary: This study proposes a dynamic ensemble selection method to deal with concept drift in imbalanced data streams. By using a novel technique to generate new instances and selecting the optimal combination based on candidate classifier performance, the proposed method outperforms others in terms of classification accuracy and tracking new concepts.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Reza Davtalab, Rafael M. O. Cruz, Robert Sabourin
Summary: Dynamic ensemble selection (DES) systems estimate the competence of each classifier and select the most competent ones for classification. However, most DES methods use K-Nearest Neighbors, which is sensitive to data distribution and requires all data to be stored. This article introduces a novel DES framework, FH-DES, that uses fuzzy hyperboxes to generate competence maps and incompetence maps for classifiers. The maps provide an assessment of the classifier's competence or incompetence level without processing previous samples, resulting in a more accurate dynamic selection system with lower computational complexity.
INFORMATION FUSION
(2024)
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
Pawel Zyblewski, Robert Sabourin, Michal Wozniak
Summary: This work focuses on connecting two rarely combined research directions - non-stationary data stream classification and data analysis with skewed class distributions. By proposing a novel framework that employs stratified bagging for training base classifiers and integrating data preprocessing and dynamic ensemble selection methods, the study aims to improve the classification of imbalanced data streams.
INFORMATION FUSION
(2021)
Article
Computer Science, Information Systems
Osama A. Mahdi, Eric Pardede, Nawfal Ali
Summary: Data stream mining is an important research topic with increasing attention in various applications. Challenges of concept drift and multiple classes in data streams have motivated the proposal of a hybrid block-based ensemble approach, which outperforms other algorithms in experimental evaluations.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Paulo R. G. Cordeiro, George D. C. Cavalcanti, Rafael M. O. Cruz
Summary: This paper proposes a novel approach called Hardness-aware Oracle with Dynamic Ensemble Selection (HaO-DES) to address the problem of optimal DES algorithm selection in different scenarios. HaO-DES evaluates and selects the best DES techniques per instance using a new measure called Hardness-aware Oracle (HaO). The experimental results show that HaO-DES outperforms or obtains similar results compared to four individual DES approaches, especially in heterogeneous pool settings.
Article
Computer Science, Artificial Intelligence
Rogerio C. P. Fragoso, George D. C. Cavalcanti, Roberto H. W. Pinheiro, Luiz S. Oliveira
Summary: This work introduces a new method called MODES, which decomposes the original multi-class problem into multiple one-class problems to provide competent classifiers for each region of the feature space. Experimental results show that this method outperforms the literature, especially for databases with complex decision regions.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Ahmad Zairi Zaidi, Chun Yong Chong, Rajendran Parthiban, Ali Safaa Sadiq
Summary: In this paper, a novel classification framework for touch-based continuous mobile device authentication (CMDA) is proposed, which utilizes dynamic selection of classifiers to improve the authentication performance. Various selection methods are evaluated, showing promising potential and feasibility in different scenarios of touch datasets.
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Meenal Jain, Gagandeep Kaur
Summary: Network security is crucial in the digital age, with research focusing on evolving and secure mechanisms for secure communications. This paper presents distributed machine learning based ensemble techniques for detecting concept drift and attacks in network traffic, achieving high accuracy on various datasets. Machine learning, coupled with new technologies, offers promising solutions to combat the ever-increasing pace of network-based attacks.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Cuie Yang, Yiu-ming Cheung, Jinliang Ding, Kay Chen Tan
Summary: This article proposes a hybrid ensemble approach to address the concept drift-tolerant transfer learning problem, adapting target domain models to new environments through class-wise weighted ensemble. The approach assigns weight vectors for classifiers from previous data chunks, allowing each class of current data to leverage historical knowledge independently.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ishwar Baidari, Nagaraj Honnikoll
Summary: The article discusses the changing distribution of data over time, proposes a method using Bhattacharyya distance for concept drift detection, and demonstrates through experiments that it improves detection and accuracy in various scenarios.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Kanu Goel, Shalini Batra
Summary: The article presents a new data stream framework DA-DDE, which responds to multiple drift types by combining online and block-based ensemble techniques, demonstrating high effectiveness in handling various drift types.
COMPUTATIONAL INTELLIGENCE
(2022)
Article
Engineering, Biomedical
Fabio A. Spanhol, Luiz S. Oliveira, Caroline Petitjean, Laurent Heutte
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2016)
Article
Computer Science, Artificial Intelligence
A. G. Hochuli, L. S. Oliveira, A. S. Britto, R. Sabourin
PATTERN RECOGNITION
(2018)
Article
Computer Science, Artificial Intelligence
Andre L. Brun, Alceu S. Britto, Luiz S. Oliveira, Fabricio Enernbreck, Robert Sabourin
PATTERN RECOGNITION
(2018)
Article
Computer Science, Artificial Intelligence
P. J. Sudharshan, Caroline Petitjean, Fabio Spanhol, Luiz Eduardo Oliveira, Laurent Heutte, Paul Honeine
EXPERT SYSTEMS WITH APPLICATIONS
(2019)
Article
Forestry
Deivison Venicio Souza, Joielan Xipaia Santos, Helena Cristina Vieira, Tawani Lorena Naide, Silvana Nisgoski, Luiz Eduardo S. Oliveira
WOOD SCIENCE AND TECHNOLOGY
(2020)
Review
Computer Science, Information Systems
Jonathan de Matos, Steve Tsham Mpinda Ataky, Alceu de Souza Britto, Luiz Eduardo Soares de Oliveira, Alessandro Lameiras Koerich
Summary: This paper reviews machine learning methods for histopathological image analysis, including shallow and deep learning methods, covering common tasks and datasets used in HI research.
Article
Chemistry, Analytical
Lucas O. Teixeira, Rodolfo M. Pereira, Diego Bertolini, Luiz S. Oliveira, Loris Nanni, George D. C. Cavalcanti, Yandre M. G. Costa
Summary: The study demonstrated the impact of lung segmentation in COVID-19 identification using CXR images, achieving good Jaccard distance and Dice coefficient for segmentation. It investigated the generalization of COVID-19 from images created from different sources, finding a strong bias introduced by underlying factors from different sources even after segmentation.
Review
Chemistry, Analytical
Yandre M. G. Costa, Sergio A. Silva, Lucas O. Teixeira, Rodolfo M. Pereira, Diego Bertolini, Alceu S. Britto Jr, Luiz S. Oliveira, George D. C. Cavalcanti
Summary: This article reviews the top-100 most cited papers in the field of COVID-19 detection from thoracic medical imaging, discussing important aspects such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and the availability of datasets and codes.
Proceedings Paper
Computer Science, Artificial Intelligence
Voncarlos Araujo, Alceu S. Britto, Andre L. Brun, Alessandro L. Koerich, Rosane Falate
2017 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
(2017)
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
Acoustics
Jhony K. Pontes, Clinton Fookes, Alceu S. Britto, Alessandro L. Koerich
2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
(2017)
Article
Computer Science, Hardware & Architecture
Eduardo K. Viegas, Altair O. Santin, Luiz S. Oliveira
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)