Article
Computer Science, Artificial Intelligence
Peter Olukanmi, Fulufhelo Nelwamondo, Tshilidzi Marwala, Bhekisipho Twala
Summary: This paper addresses two key challenges of k-means clustering. In the first part, it provides estimates for the range of k required for clustering and improves the efficiency of the k-means algorithm through automation. In the second part, it incorporates automatic outlier detection into the k-means process, solving a previous problem regarding complete automation.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Merhad Ay, Lale Ozbakir, Sinem Kulluk, Burak Guelmez, Gueney Oztuerk, Sertay Ozer
Summary: Clustering is a data mining method that divides large-sized data into subgroups based on similarities. The FC-Kmeans algorithm, proposed in this paper, allows clustering by fixing some cluster centers while considering real conditions. Experimental results show that although the FC-Kmeans algorithm has more limitations, it performs similarly to the K-means algorithm in terms of performance indicators.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Kuntal Chowdhury, Debasis Chaudhuri, Arup Kumar Pal
Summary: This paper discusses the concept of clustering and an optimization method for the initialization process of the K-means algorithm, proposing a new entropy-based approach. It also introduces an algorithm for calculating the correct number of clusters in datasets and compares it with other methods.
NEURAL COMPUTING & APPLICATIONS
(2021)
Review
Computer Science, Information Systems
Abiodun M. Ikotun, Absalom E. Ezugwu, Laith Abualigah, Belal Abuhaija, Jia Heming
Summary: Advances in data collection techniques have enabled the accumulation of large quantities of data. The K-means algorithm, while popular, has challenges such as determining the number of clusters and detecting non-Euclidean shapes. Research efforts have been made to improve its performance and robustness.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Mustafa Jahangoshai Rezaee, Milad Eshkevari, Morteza Saberi, Omar Hussain
Summary: This paper introduces a game-based k-means (GBK-means) algorithm that competes cluster centers to attract data for more accurate clustering. Experimental results demonstrate the superiority of GBK-means over traditional clustering algorithms.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yongxuan Lai, Songyao He, Zhijie Lin, Fan Yang, Qifeng Zhou, Xiaofang Zhou
Summary: This article proposes a new framework that generates base partitions in an unsupervised manner and assigns different weights to each cluster of the base partitions. The weighted co-association matrix based consensus approach is then applied to achieve a final partition. Empirical results show that the new framework retains high accuracy, adaptability, and robustness.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Physics, Multidisciplinary
Ailin Zhu, Zexi Hua, Yu Shi, Yongchuan Tang, Lingwei Miao
Summary: The paper proposes an improved k-means algorithm based on evidence distance, which achieves better clustering effect and faster algorithm convergence by replacing Euclidean distance with evidence distance.
Article
Computer Science, Information Systems
Kristina P. Sinaga, Ishtiaq Hussain, Miin-Shen Yang
Summary: The study introduces a new clustering algorithm called Entropy-k-means that can achieve feature reduction behavior without being affected by initial cluster settings. This algorithm automatically finds the optimal number of clusters by eliminating irrelevant features.
Article
Computer Science, Information Systems
Nawaf H. M. M. Shrifan, Muhammad F. Akbar, Nor Ashidi Mat Isa
Summary: K-means is a popular clustering algorithm, but its performance is hindered by outliers in real datasets. This paper presents a novel modification of the algorithm based on Tukey's rule and a new distance metric, which significantly improves clustering accuracy and centroids convergence.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Review
Chemistry, Multidisciplinary
Abiodun M. Ikotun, Mubarak S. Almutari, Absalom E. Ezugwu
Summary: The K-means clustering algorithm is widely used in traditional clustering for its simplicity and low computational complexity. Recent advancements include the use of nature-inspired optimization techniques to improve its performance in handling automatic data clustering. Studies have shown that the K-means algorithm surpasses other clustering algorithms in terms of speed, accessibility, simplicity, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Carlo Baldassi
Summary: We introduce an evolutionary algorithm called recombinator-k-means for optimizing the highly nonconvex kmeans problem. Its defining feature is that its crossover step involves all the members of the current generation, stochastically recombining them with a repurposed variant of the k-means++ seeding algorithm. The recombination also uses a reweighting mechanism that realizes a progressively sharper stochastic selection policy and ensures that the population eventually coalesces into a single solution. We compare this scheme with a state-of-the-art alternative, a more standard genetic algorithm with deterministic pairwise-nearest-neighbor crossover and an elitist selection policy, of which we also provide an augmented and efficient implementation. Extensive tests on large and challenging datasets (both synthetic and real word) show that for fixed population sizes recombinator-k-means is generally superior in terms of the optimization objective, at the cost of a more expensive crossover step. When adjusting the population sizes of the two algorithms to match their running times, we find that for short times the (augmented) pairwise-nearest-neighbor method is always superior, while at longer times recombinator-k-means will match it and, on the most difficult examples, take over. We conclude that the reweighted whole-population recombination is more costly but generally better at escaping local minima Moreover, it is algorithmically simpler and more general (it could be applied even to k-medians or k-medoids, for example).
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Yi-Cheng Chen, Yen-Liang Chen, Jyun-Yun Lu
Summary: K-Means algorithm is one of the most famous and popular clustering algorithms in the world, known for its simple structure, easy implementation, high efficiency, and fast convergence speed. This article introduces an improvement to past variants of K-Means used in evolutionary clustering, considering both past and future clustering results, and extending K-Means to multiple cycles, resulting in more consistent, stable, and smooth clustering results.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Construction & Building Technology
Ana Maria Bueno, Inaiele Mendes da Luz, Iasmin Lourenco Niza, Evandro Eduardo Broday
Summary: This study evaluated the relationship between thermal dissatisfaction and productivity in university classrooms and found that thermal dissatisfaction has an impact on students' productivity.
BUILDING AND ENVIRONMENT
(2023)
Article
Geosciences, Multidisciplinary
Junfa Xie, Xingrong Xu, Yang Lan, Xiaoqian Shi, Yundong Yong, Dunshi Wu
Summary: In this study, an unsupervised weighted k-means clustering velocity-picking method is proposed to pick seismic velocities by selecting the centers of energy clusters. This method works on the semblance velocity spectrum and requires an initial velocity function and three user-defined thresholds to limit the search area.
FRONTIERS IN EARTH SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Pu Zhang, Changjing Shang, Qiang Shen
Summary: This article presents a novel approach for computing interpolated outcomes with TSK models, which improves computational efficiency and minimizes the adverse impact on accuracy. It introduces a rule-clustering-based method for selecting rules for large rule bases. Experimental results show the effectiveness of the introduced techniques.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Feng Liu, Arif Ahmed Sekh, Chai Quek, Geok See Ng, Dilip K. Prasad
Summary: This paper introduces a hybrid fuzzy-rough set approach called RS-HeRR for generating effective, interpretable, and compact rule sets. It combines a powerful rule generation and reduction fuzzy system and improves system performance by reducing partial dependencies in rules.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
W. H. Chai, S. S. Ho, H. C. Quek
Summary: A fast parallelable Jacobi iteration type optimization method is proposed for non-smooth convex composite optimization, which integrates both first and second-order techniques to boost convergence speed. Experimental results show that the proposed method converges significantly better than existing methods.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Information Systems
Woon Huei Chai, Shen-Shyang Ho, Hiok Chai Quek
Summary: This paper studies the recovery probability of the state-of-the-art sparse recovery method YALL1 and provides a generalization of a theoretical work based on a special case of YALL1 optimization problem. The results show that not only the special case but also some other cases of YALL1 optimization problem can recover any sufficiently sparse coefficient vector under certain conditions. The trade-off parameter in YALL1 allows the recovery probability to be optimally tuned. Experimental results demonstrate the superiority of YALL1 optimization problem with primal augmented Lagrangian optimization technique in terms of speed.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Qing Yang Eddy Lim, Qi Cao, Chai Quek
Summary: This study introduces Reinforcement Learning and dynamic portfolio rebalancing to enhance portfolio management efficiency, adapting portfolios dynamically to market trends, risks, and returns. After evaluating and comparing three constructed financial portfolios, it was found that the RL agent for gradual portfolio rebalancing with the LSTM model outperformed other methods, improving returns by 27.9-93.4% compared to full rebalancing methods.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Jeow Li Huan, Arif Ahmed Sekh, Chai Quek, Dilip K. Prasad
Summary: This paper investigates text classification methods by using deep models and recurrent neural networks to extract features and represent documents as semantic vector sequences for classification. The addition of sentiment information improves accuracy, outperforming classical techniques in experiments.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Yue Hu, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek, Quanjun Yin
Summary: This paper proposes a neural network model called STEM-COVID to identify asymptomatic COVID-19 cases (ACCs) using contact tracing data. The model incorporates adaptive resonance theory and weighted evidence pooling to achieve high accuracy and efficiency in identifying ACCs. It also demonstrates robustness against noisy data and breakthrough infections after vaccination.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Xie Chen, Deepu Rajan, Dilip K. Prasad, Chai Quek
Summary: This paper investigates the benefits of using a deep learning model as a fuzzy implication operator in a neuro-fuzzy system for learning and explaining predictions of both steady-state and dynamically changing data. The results show that this approach improves the model performance and enhances the interpretability of the reasoning process.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Marcin Bialas, Jacek Mandziuk
Summary: This article proposes an effective variant of STDP extended by an activation-dependent scale factor, serving as an efficient mechanism for the unsupervised development of RFs. The importance of synaptic scaling and lateral inhibition in the successful development of RFs is demonstrated, with the significance of maintaining high levels of synaptic scaling highlighted. Experimental results show that the proposed solution performs well in classification tasks on the MNIST dataset.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek
Summary: Hierarchical reinforcement learning is an approach to decompose goals into subgoals for long-horizon goal-reaching tasks. End-to-end HRL methods use a hierarchy of policies to search useful subgoals directly in a continuous subgoal space. LIDOSS, an integrated subgoal discovery heuristic, reduces the search space of the higher-level policy by focusing on subgoals with a higher probability of occurrence in state-transition trajectories leading to the goal.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Leon Lai Xiang Yeo, Qi Cao, Chai Quek
Summary: This article introduces two trading indicators: the optimized fMACDH indicator and the fMACDH-fRSI indicator. These two indicators are optimized using a genetic algorithm, and the optimized fMACDH indicator is used to propose two rule-based portfolio rebalancing algorithms: Tactical Buy and Hold (TBH) and Rule-Based Business Cycle (RBBC). The experiments show consistent and encouraging performances of these algorithms in dynamic portfolio rebalancing.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Yizhang Wang, Di Wang, You Zhou, Xiaofeng Zhang, Chai Quek
Summary: The VDPC algorithm is proposed to address the limitation of DPC in identifying clusters with variational density. It systematically performs the clustering task on datasets with different density distributions by identifying representatives, constructing initial clusters, and using a unified clustering framework.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek
Summary: This article proposes a novel subgoal graph-based planning method called LSGVP, which addresses the challenge of learning to reach long-horizon goals in spatial traversal tasks for autonomous agents. LSGVP uses a subgoal discovery heuristic based on cumulative reward and automatically prunes the learned subgoal graph to remove erroneous connections. It achieves higher cumulative positive rewards and goal-reaching success rates compared to other subgoal sampling or discovery heuristics.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xinjing Song, Di Wang, Chai Quek, Ah-Hwee Tan, Yanjiang Wang
Summary: This paper proposes a cognitive model called STEM-ADL, which encodes event sequences to predict the type and starting time of daily self-care activities. Experimental results demonstrate that STEM-ADL outperforms other models and is suitable for real-life healthcare applications.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Wai-Cheong Lincoln Lew, Di Wang, Kai Keng Ang, Joo-Hwee Lim, Chai Quek, Ah-Hwee Tan
Summary: This article proposes a video summarization model (EVES) based on EEG and video emotion data, which utilizes multimodal deep reinforcement learning architecture to learn visual interestingness for better video summaries. The experimental results show that EVES outperforms unsupervised models and narrows the performance gap with supervised models. EVES receives higher ratings in content coherency and emotion-evoking content.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Anton Lee, Heitor Murilo Gomes, Yaqian Zhang
Summary: Balancing the stability-plasticity trade-off is crucial in continual learning. Instead of using a constant hyper-parameter, we propose a dynamic method to balance stability and plasticity, which has shown improved performance in multiple benchmarks.
2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2022)
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)
