Article
Computer Science, Artificial Intelligence
Xin Guan, Yoshikazu Terada
Summary: In this paper, a novel sparse kernel k-means clustering method is proposed to address the issue of clustering high-dimensional data. By optimizing the feature indicators, the clustering performance is improved.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Guoquan Li, Linxi Yang, Kequan Zhao
Summary: This paper proposes a unified model based on the generalized l(q)-norm to address the challenge of optimal scoring on small sample size datasets, and develops an efficient alternative direction method of multipliers to handle the difficulties in dealing with the generalized norm. Numerical experiments demonstrate the effectiveness and feasibility of the proposed method.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Saptarshi Chakraborty, Swagatam Das
Summary: In this paper, a simple and efficient sparse clustering algorithm called LW-k-means is proposed for high-dimensional data. The algorithm incorporates feature weighting to enable feature selection and has a time complexity similar to traditional algorithms. The strong consistency of the LW-k-means procedure is also established. Experimental results on synthetic and real-life datasets demonstrate that LW-k-means performs competitively in terms of clustering accuracy and computational time compared to existing methods for center-based high-dimensional clustering.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Guoquan Li, Xuxiang Duan, Zhiyou Wu, Changzhi Wu
Summary: The paper proposes a generalized elastic net optimal scoring problem (GenOS) to find sparse discriminant vectors. By imposing l(q)-norm in GenOS to confer sparsity to discriminant vectors and adding l(2)-norm to enhance the performance of the classifier, a new efficient algorithm is developed to solve this problem.
Article
Computer Science, Artificial Intelligence
Tingquan Deng, Yang Huang, Ge Yang, Changzhong Wang
Summary: Feature selection is an effective dimensionality reduction method. Existing methods neglect the different classification abilities of features when constructing feature evaluation functions. This paper proposes a pointwise mutual information sparsely embedded feature selection model, which utilizes fuzzy information granules and a classification information matrix to describe the discernibility of features and the nonlinear relationship between the feature space and decision space. The model learns the information fusion coefficients adaptively to avoid overfitting.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Theory & Methods
Diwakar Tripathi, B. Ramachandra Reddy, Alok Kumar Shukla
Summary: This article explores the issue of high-dimensional data in credit scoring, proposing a collaborative feature ranking approach that improves the classification performance of most classifiers. The proposed method is compared with existing feature ranking approaches on five credit scoring datasets, showing enhancements in feature selection and classification accuracy.
Article
Computer Science, Information Systems
Madhukar G. Rao, Ramesh Dharavath
Summary: This paper introduces a model for weather prediction using deep learning, which leverages the advancements in big data to enhance prediction accuracy. Through pre-processing and feature extraction with PCA, combined with BBOA and deep autoencoder, the model has successfully improved prediction accuracy.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Health Care Sciences & Services
Aneta Polewko-Klim, Krzysztof Mnich, Witold R. Rudnicki
Summary: A hybrid protocol integrating clinical and molecular data was proposed for more effective classification of cancer patients. This method yielded promising results in predicting clinical endpoints for breast cancer and urothelial bladder carcinoma samples.
JOURNAL OF MEDICAL SYSTEMS
(2021)
Article
Computer Science, Information Systems
Himansu Das, Bighnaraj Naik, H. S. Behera
Summary: This paper proposes a feature selection approach based on Jaya optimization algorithm, which improves the performance of supervised machine learning techniques by reducing the dimensions of the feature space. Experimental results show that this approach achieves higher classification accuracy compared to other feature selection methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Biology
Akshata K. Naik, Venkatanareshbabu Kuppili
Summary: Gene selection is crucial for classifying high-dimensional microarray gene expression data. This paper proposes a neural network-based embedded feature selection method called Weighted GCNN (WGCNN), which can capture non-linear interactions and solve multi-class problems. The WGCNN incorporates feature weighting and statistical guided dropout to avoid overfitting. Experimental validation demonstrates that the WGCNN performs well in terms of F1 score and number of features selected.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Zahra Atashgahi, Ghada Sokar, Tim van der Lee, Elena Mocanu, Decebal Constantin Mocanu, Raymond Veldhuis, Mykola Pechenizkiy
Summary: A novel unsupervised feature selection method QuickSelection is proposed in this paper, which introduces the concept of neuron strength in sparse neural networks and combines it with sparsely connected denoising autoencoders to derive the importance of all input features. The method achieves the best trade-off of classification and clustering accuracy, running time, and maximum memory usage on benchmark datasets, with considerable speed increase, memory reduction, and the least amount of energy consumption compared to other state-of-the-art autoencoder-based feature selection methods.
Article
Computer Science, Information Systems
Libo Yang, Dawei Zhu, Xuemei Liu, Pei Cui
Summary: In this study, we propose a robust feature selection method based on sparse regression. By utilizing a non-square form of the L2,1 norm as both the loss function and regularization term, the model's resistance to outliers is effectively enhanced while achieving feature selection simultaneously. Experimental results on three public datasets demonstrate that our method outperforms other comparison methods.
Article
Computer Science, Artificial Intelligence
Motahare Akhavan, Seyed Mohammad Hossein Hasheminejad
Summary: A new two-phase gene selection method for microarray data is proposed in this study. This method reduces the number of genes significantly and improves the classification accuracy through anomaly detection and guided genetic algorithm.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Multidisciplinary Sciences
Hiroki Saishu, Kota Kudo, Yuichi Takano
Summary: This paper introduces a mixed-integer optimization approach for sparse Poisson regression, allowing to find the best subset of explanatory variables. By applying a piecewise-linear approximation to the log-likelihood function, a mixed-integer quadratic optimization formulation is derived and can be solved to optimality using optimization software. Experimental results show that our method outperforms conventional greedy algorithms in selecting tangent lines and provides better out-of-sample prediction performance in low-noise situations.
Article
Computer Science, Artificial Intelligence
Jhoseph Jesus, Anne Canuto, Daniel Araujo
Summary: Feature selection is crucial in machine learning, using multiple criteria to determine the best attribute subset can yield encouraging results. In data noise scenarios, the pareto-front based dynamic feature selection (PF-DFS) method shows more stability and robustness compared to other methods.
APPLIED SOFT COMPUTING
(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)