Article
Computer Science, Artificial Intelligence
Omar A. M. Salem, Feng Liu, Yi-Ping Phoebe Chen, Ahmed Hamed, Xi Chen
Summary: This paper investigates the challenge of data quality in classification problems and proposes an instance selection method based on uncertainty region (ISUR) and a feature selection method called fuzzy joint mutual information feature selection based on uncertainty region (FJMIUR). Experimental results demonstrate the superior performance of FJMIUR across multiple classification measures.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Lazhar Farek, Amira Benaidja
Summary: Feature selection is a crucial preprocessing step for text categorization. We propose a new method called CFSRR, which uses mutual information to evaluate the importance of each feature with respect to its co-occurring ones. Empirical results show the efficiency and robustness of CFSRR on multiple datasets.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Omar A. M. Salem, Feng Liu, Yi-Ping Phoebe Chen, Xi Chen
Summary: Improving classification performance is a major challenge in various real-world applications, with feature selection being a powerful solution to address issues caused by irrelevant and redundant features. The proposed Fuzzy Joint Mutual Information (FJMI) method overcomes limitations of existing methods and shows promising improvements in classification performance, feature selection stability, and number of selected features on benchmark datasets.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2021)
Article
Computer Science, Information Systems
Tapas Bhadra, Sanghamitra Bandyopadhyay
Summary: The paper proposes a novel supervised feature selection approach based on dense subgraph discovery. The algorithm proceeds in two phases to select features with maximal average class relevance, minimal average pairwise redundancy, and good discriminating power. Experimental results show the proposed approach is competitive with conventional and state-of-the-art algorithms in supervised feature selection.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Kexin Yin, Junren Zhai, Aifeng Xie, Jianqi Zhu
Summary: Feature selection algorithms based on three-way interaction information have been widely studied, but most traditional algorithms only consider class-dependent redundancy, which can underestimate redundancy. To address this issue, a feature selection algorithm based on maximum dynamic relevancy minimum redundancy is proposed. The algorithm introduces a quality coefficient to estimate feature relevancy and class-independent redundancy to fully consider redundancy, and adaptive coefficients are proposed to optimize the algorithm. Experimental comparisons on 19 benchmark datasets with six algorithms demonstrate that the proposed algorithm outperforms others in terms of performance.
PATTERN ANALYSIS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Francisco Macedo, Rui Valadas, Eunice Carrasquinha, M. Rosario Oliveira, Antonio Pacheco
Summary: Feature selection is an essential preprocessing technique to improve regression and classification tasks by reducing dimensionality. DMIM method, based on Decomposed Mutual Information Maximization, overcomes complementarity penalization issue in existing methods, showing better classification performance in experiments.
Article
Automation & Control Systems
Mariusz Kubkowski, Jan Mielniczuk, Pawel Teisseyre
Summary: Traditional conditional independence tests based on conditional mutual information may lose power when the conditioning set is large in dealing with discrete data. To overcome this drawback, a method based on Short Expansion of Conditional Mutual Information (SECMI) is proposed, which has significantly higher power on discrete data.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Francisco Souza, Cristiano Premebida, Rui Araujo
Summary: This paper presents a novel feature selection method based on conditional mutual information. The method incorporates high order dependencies into the feature selection process and speeds up the process through a greedy search procedure. Experimental results show that the proposed method outperforms other algorithms in terms of accuracy and speed.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Anurag Tiwari, Amrita Chaturvedi
Summary: The widespread use of feature selection in various fields emphasizes its importance in expert and intelligent systems. Conventional methods suffer from poor classification accuracy and high computational cost, while hybrid methods offer better efficiency and scalability. A new hybrid feature selection method, IFS-DBOIM, was introduced to address these issues, showing improved performance in classification accuracy with fewer features.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Peng Zhou, Ni Wang, Shu Zhao
Summary: This paper focuses on the interaction of features within and between streaming groups, proposing an Online Group Streaming Feature Selection method named OGSFS-FI, which consists of two stages: online intra-group selection and online inter-group selection. The method utilizes a new pair selection strategy and the elastic net method for efficient and effective feature selection.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Emrah Hancer
Summary: Fuzzy mutual information is a popular method in information theory for quantifying the information between random variables, capable of handling different types of variables effectively. Recently, it has been integrated into evolutionary filter feature selection approaches to significantly improve the computational efficiency and performance of classification algorithms on real-world datasets.
Article
Computer Science, Artificial Intelligence
Zhaolong Ling, Ying Li, Yiwen Zhang, Kui Yu, Peng Zhou, Bo Li, Xindong Wu
Summary: Causal feature selection has received increasing attention. However, existing algorithms have high computational complexity. To address this, this paper proposes a novel algorithm called CFS-MI, which analyzes the unique performance of causal features in mutual information and reduces computational complexity by separating pairwise comparisons in two stages. Experimental results demonstrate that CFS-MI achieves comparable accuracy and superior computational efficiency compared to 7 state-of-the-art algorithms.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Jinghua Liu, Songwei Yang, Yaojin Lin, Chenxi Wang, Cheng Wang, Jixiang Du
Summary: Multi-label feature selection is an important task in processing multi-semantic high-dimensional data. This paper proposes a new algorithm called FSEP, which considers label significance and pairwise label correlations. The proposed method achieves encouraging results compared with state-of-the-art MFS algorithms, as demonstrated by extensive experiments.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Wassim El-Hajj, Hazem Hajj
Summary: This paper proposes a new feature selection method called MFX, which optimally selects a subset of features by mathematically formulating the selection problem as an optimization problem. MFX considers both classification accuracy and feature discriminability, and has two distinguishing features of treating all documents from the same category as one extended document and choosing discriminative terms that are frequent within the category and rare in other categories. Experimental results on various datasets demonstrate the superiority of MFX over other methods, and its performance is shown to outperform recent text classification algorithms based on neural networks and word embeddings when combined with the Support Vector Machine (SVM) classifier.
COMPUTER SPEECH AND LANGUAGE
(2022)
Article
Computer Science, Artificial Intelligence
Minlan Pan, Zhanquan Sun, Chaoli Wang, Gaoyu Cao
Summary: This paper discusses the challenges of high-dimensional multi-label data and the popularity of filtering feature selection algorithms in pattern recognition and machine learning. It introduces the research topic of multi-label feature selection based on mutual information and proposes an approximation method that considers label correlation without significantly increasing computational cost.
INTELLIGENT DATA ANALYSIS
(2022)
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)