Article
Computer Science, Information Systems
Farah Jemili
Summary: Intelligent intrusion detection system is a promising technique for securing computer networks due to the rapid evolution of attacks and network growth. Individual classification methods have proven to be inefficient in providing good detection rates and reducing false alarm rates. In this study, a hybrid approach based on the stacking scheme is proposed, which combines the strengths of neuro-fuzzy and genetic-fuzzy methods to maximize detection rates and reduce false alarm rates effectively.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Sajal Debnath, Md Manjur Ahmed, Samir Brahim Belhaouari, Toshiyuki Amagasa, Mostafijur Rahman
Summary: In the age of a technological revolution, the online classification of data generated from heterogeneous sources plays a crucial role in data mining and analysis. Fuzzy-system-based (FSB) classifiers have shown remarkable contributions due to their antecedent-consequent rule base structure. However, predefined membership functions may not be suitable for online or stream data. This paper presents a novel buffer-based adaptive fuzzy classifier (BAFC) algorithm that utilizes data-clouds to handle the dynamic nature of stream data and addresses storage problems.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Gabriella Casalino, Giovanna Castellano, Ciro Castiello, Corrado Mencar
Summary: Fuzziness has an impact on the behavior of Fuzzy Rule-Based Classifiers, but only in regions where the classification confidence is low. Therefore, in Explainable Artificial Intelligence, fuzziness is beneficial in FRBCs only when accompanied by an explanation of the output confidence.
Article
Engineering, Biomedical
Jingdong Yang, Peng Liu, Yifei Meng, Xiaolin Zhang, Shaoqing Yu
Summary: This study proposes a Feature-Block classification model (MBLCC) based on Label-Links Classifier Chain for the diagnosis of allergic rhinitis. It partitions allergic rhinitis instances with similar characteristics, builds an ordered classification chain, and integrates the predictions of each block classifiers using evidence theory. The cross-validation experiments show that MBLCC achieves high evaluation indicators (sensitivity, specificity, accuracy, F1-score, and G-Mean) of 91.80%, 96.8%, 96.9%, 0.925, and 0.941 respectively. It outperforms other baselines in generalization performance and provides more effective and rapid diagnosis of rhinitis.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Hamidreza Kadkhodaei, Amir Masoud Eftekhari Moghadam, Mehdi Dehghan
Summary: This paper presents a distributed heterogeneous ensemble classifier for big data, which utilizes multiple classifiers to achieve more accurate data classification. Experimental results indicate the superiority of the proposed method in terms of classification accuracy, performance, and scalability compared to existing ensemble algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Danyang Li, Zhuhong Zhang, Guihua Wen
Summary: Ensemble pruning improves system performance and reduces storage requirements in integration systems. Most approaches evaluate the competence and relationships of classifiers by analyzing their predictions to remove low-quality or redundant classifiers. However, finding the best way to represent classifiers and create ensemble diversity remains a research problem. To address this, we propose a new classifier selection method called CRCEEP, which incorporates two new classifier representation learning methods and a clustering ensemble method. Extensive experiments on UCI datasets demonstrate the effectiveness of CRCEEP and the importance of classifier representation.
APPLIED INTELLIGENCE
(2023)
Article
Mathematics
Jinghong Zhang, Yingying Li, Bowen Liu, Hao Chen, Jie Zhou, Hualong Yu, Bin Qin
Summary: With the expansion of data scale and diversity, the issue of class imbalance has become increasingly salient. To address these challenges, a novel fuzzy classifier is proposed that can handle classification tasks with class-imbalanced data.
Article
Chemistry, Multidisciplinary
Hanaa Salem, Mahmoud Y. Shams, Omar M. Elzeki, Mohamed Abd Elfattah, Jehad F. Al-Amri, Shaima Elnazer
Summary: This paper proposes an algorithm for diabetes classification in pregnant women using the Pima Indians Diabetes Dataset. The algorithm includes a preprocessing step to enhance the dataset's quality, a fuzzy KNN classifier with modified membership functions, and a grid search method to tune the classifier. The proposed TFKNN classifier outperforms other classifiers and has high performance in terms of accuracy, specificity, precision, and average AUC.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Zekang Bian, Jin Zhang, Yusuke Nojima, Fu-lai Chung, Shitong Wang
Summary: Due to its distinguished nonlinear mapping capability and interpretability, a novel hybrid-ensemble-based imbalanced interpretable TSK fuzzy classifier (HI-TSK-FC) is proposed in this study to achieve enhanced generalization and better interpretability. The HI-TSK-FC integrates an imbalanced global linear regression sub-classifier (IGLRc) and several imbalanced TSK fuzzy sub-classifiers (I-TSK-FCs). The training method of HI-TSK-FC, called imbalanced residual sketch learning (IRSL), is devised to share the virtues of both deep and wide learning.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
J. Sanz, M. Sesma-Sara, H. Bustince
Summary: This paper introduces a new fuzzy association rule-based classifier, FARCI, for directly tackling imbalanced classification problems. Experimental results show the superiority of the new method in terms of performance, F-score, and rule base size when compared to other algorithm modification approaches.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Software Engineering
Anna Czmil, Jacek Kluska, Sylwester Czmil
Summary: In this work, a Python-based implementation of a simple classifier called GPR is presented, which combines gene expression programming (GEP) features and the algebraic representation of the 'if-then' fuzzy rules theory of the Takagi-Sugeno fuzzy inference system. The generated fuzzy metarules are highly interpretable and suitable for a wide range of applications. The open-source Python implementation of the GPR algorithm allows for its use without any commercial software tools and provides open access to the research community. Enhancements have been made to improve the readability and interpretability of the rules.
Article
Computer Science, Information Systems
Jacek Kluska, Michal Madera
Summary: The paper presents a new design of a very simple data-driven binary classifier and evaluates its performance empirically, showing that it performs comparably well to popular machine learning methods. The newly introduced classifier performed surprisingly well on 16 datasets, with high interpretability demonstrated through examples of classification models.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Fei Gao
Summary: Sparse rule base is a common problem in fuzzy rule-based systems. This paper presents a density-based fuzzy rule interpolation method that adaptively selects the closest rules with high similarity to the inputs. The method has been verified through fifteen classification benchmarks, demonstrating its effectiveness and efficiency.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Interdisciplinary Applications
R. V. Darekar, Meena Suhas Chavand, S. Sharanyaa, Nihar M. Ranjan
Summary: This research proposes a new automated speech emotion recognition model. It filters the raw speech data and segments the signals into frames. It then recovers the Spectrogram feature using Convolutional Neural Network (CNN). The acoustic and Spectrogram features are combined to create a hybrid feature vector. The model uses an ensemble-of-classifiers consisting of Recurrent Neural Network (RNN), DBN, and Artificial Neural Network (ANN) for speech emotion recognition.
ADVANCES IN ENGINEERING SOFTWARE
(2023)
Article
Computer Science, Artificial Intelligence
Amgad M. Mohammed, Enrique Onieva, Michal Wozniak, Gonzalo Martinez-Munoz
Summary: This article discusses the strategy of classifier ensemble pruning, involving optimizing predefined performance criteria to identify subensembles. The study analyzes a set of heuristic metrics to guide the pruning process, with results indicating that ordered aggregation is an effective strategy for improving predictive performance and reducing computational complexities.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Suhad Lateef Al-Khafaji, Jun Zhou, Xiao Bai, Yuntao Qian, Alan Wee-Chung Liew
Summary: In this paper, a novel method for boundary detection in close-range hyperspectral images is proposed. The method effectively predicts the boundaries of objects with similar color but different materials. By estimating the spatial distribution of spectral responses and using abundance maps and spectral feature vectors, the method constructs a boundary map. Experimental results show that the proposed method outperforms alternative methods when dealing with boundaries of objects with similar color but different materials.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Business
Jaliya Amarasinghe Arachchige, Sara Quach, Eduardo Roca, Benjamin Liu, Alan Wee-Chung Liew, George Earl
Summary: This research adopts the conservation of resources theory and explores the joint influence of multiple factors on consumers' housing choices. Using an innovative Machine Learning approach, the study identifies the most significant set of factors affecting housing choices. The results indicate that energy resources are the primary constraint, while other personal, conditional, and object resources are secondary. The findings have important implications for real estate investors, policymakers, and other stakeholders in developing better and tailored housing strategies.
JOURNAL OF CONSUMER BEHAVIOUR
(2022)
Editorial Material
Computer Science, Information Systems
Remco Dijkman, Samira Si-said Cherfi, Rik Eshuis, Alan Wee-Chung Liew
INFORMATION SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Ye Tao, Can Wang, Alan Wee-Chuang Liew, Sebastian Binnewies
Summary: Recommendation system is designed to address the problem of information overload. Ensemble methods can significantly improve the performance of a single recommendation system. This study proposes a novel user-agnostic ensemble model that enhances the performance of base recommenders by learning representations from input sequences, without requiring user IDs or meta-data.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Fang-Qi Li, Rui-Jie Zhao, Shi-Lin Wang, Li-Bo Chen, Alan Wee-Chung Liew, Weiping Ding
Summary: The pervasive deployment of the Internet of Things has brought significant impact to manufacturing and living, but security remains a crucial challenge. The detection of malicious network traffic is a common yet destructive threat. To address this, a fuzzy system incorporating Bayesian possibilistic clustering and ensemble learning is proposed to enhance security and stability.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Linqing Huang, Wangbo Zhao, Alan Wee-Chung Liew, Yang You
Summary: Remote sensing image scene classification uses CNN to extract discriminative features for classification. Different color spaces can affect the training results of CNN differently, so we propose an ECMS method that combines multiple color spaces to improve classification performance.
INFORMATION FUSION
(2023)
Article
Materials Science, Multidisciplinary
Seyedeh Alieh Kazemi, Samuel Akinlolu Ogunkunle, Oscar Allen, William Wen, Alan Wee-Chung Liew, Shiwei Yin, Yun Wang
Summary: In this study, density functional theory (DFT) was applied to investigate the halogenation effects on the properties of MXenes. The results show that the adsorption site of halogen terminals significantly affects the properties of MXene. All the halogenated MXenes considered in this study are metallic, and the electronic and mechanical properties are strongly dependent on the electronegativity of the halogen terminal groups. Therefore, selecting specific halogen terminal groups can tune the physicochemical properties of MXenes for practical applications.
JOURNAL OF PHYSICS-MATERIALS
(2023)
Article
Computer Science, Artificial Intelligence
Can Wang, Chi-Hung Chi, Lina Yao, Alan Wee-Chung Liew, Hong Shen
Summary: This paper proposes the method of interdependence analysis to capture the functional multifarious relationships among attributes and among objects in heterogeneous data. By considering the coupling context and coupling weights, it forms attribute-based and object-based coupled data representation schemes. Experimental results demonstrate that this method effectively captures global couplings.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Anh Vu Luong, Tien Thanh Nguyen, Kate Han, Trung Hieu Vu, John McCall, Alan Wee-Chung Liew
Summary: With the breakthrough of Deep Neural Networks, multi-layer architecture has influenced ensemble learning. In 2017, Zhou and Feng introduced a deep random forest called gcForest. However, its input features do not ensure better performance in layer-by-layer architecture. To address this, we propose a novel deep ensemble model with a feature generation module. We integrate weights on classifiers' outputs and encode them using variable-length encoding and optimize their values using a Particle Swarm Optimization method. Experimental results on UCI datasets show the superiority of the proposed method over benchmark algorithms.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Lingxiao Zhou, Luchen Chang, Jie Li, Quanzhou Long, Junjie Shao, Jialin Zhu, Alan Wee-Chung Liew, Xi Wei, Wanlong Zhang, Xiaocong Yuan
Summary: This study proposes the use of optical neural networks for the auxiliary diagnosis of thyroid nodules, achieving high accuracy in the classification and detection tasks. The all-optical neural network shows potential in the field of medical image processing.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2023)
Article
Business, Finance
Wendi Zhang, Bin Li, Alan Wee-Chung Liew, Eduardo Roca, Tarlok Singh
Summary: This study predicts the US REIT market using the GMDH neural network and compares its accuracy with the traditional GARCH model. The findings show that GMDH outperforms GARCH in terms of accuracy, providing more precise predictions of REIT prices. The size of training samples and the choice of kernel functions in the GMDH model also influence the accuracy of predictions.
FINANCIAL INNOVATION
(2023)
Article
Computer Science, Artificial Intelligence
Linqing Huang, Wangbo Zhao, Yong Liu, Duo Yang, Alan Wee-Chung Liew, Yang You
Summary: For cross-domain pattern classification, the proposed EMDA method effectively utilizes information from single-source and multiple target domains to improve classification performance. It aligns the distributions of source and target domains and uses labeled source domain data to classify unlabeled patterns in target domains. By combining soft classification results and discounting their weighting factors based on distribution discrepancy, EMDA achieves reliable class decisions. Experimental results on benchmark datasets demonstrate the effectiveness of EMDA compared to other advanced domain adaptation methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Fang-Qi Li, Shi-Lin Wang, Alan Wee-Chung Liew
Summary: This paper introduces the linear functionality equivalence attack, which can adapt to different network architectures without requiring knowledge of either the watermark or data. We also propose NeuronMap, a framework that can efficiently neutralize linear functionality equivalence attacks and enhance the robustness of existing white-box watermarks.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Computer Science, Information Systems
Dylan M. Janssen, Wayne Pullan, Alan Wee-Chung Liew
Summary: This paper presents a visualization tool called ECvis that assists in the development of population-based numerical optimization algorithms. The tool provides a simple interface with three modes: Density mode, Statistical mode, and Ranges mode. Through examples, the usefulness of ECvis in optimizing high dimensional functions using differential evolution is demonstrated.
Article
Chemistry, Multidisciplinary
Seyedeh Alieh Kazemi, Sadegh Imani Yengejeh, Samuel Akinlolu Ogunkunle, Lei Zhang, William Wen, Alan Wee-Chung Liew, Yun Wang
Summary: Monolayers of transition metal dichalcogenides (TMD) have excellent mechanical and electrical characteristics. This study investigates the effects of vacancies on the electrical and mechanical properties of TMDs through first-principles density functional theory (DFT). The results show that anion vacancy defects have a slight impact on the properties, while vacancies in metal complexes significantly affect the electronic and mechanical properties. The study also reveals that the mechanical properties of TMDs are influenced by their structural phases and anions.
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
