Article
Automation & Control Systems
Patricia Wollstadt, Sebastian Schmitt, Michael Wibral
Summary: Selecting a minimal feature set that provides maximum information about a target variable is a crucial task in machine learning and statistics. However, traditional information theory lacks a rigorous definition for addressing feature relevancy. The recent introduction of the partial information decomposition (PID) framework allows for quantifying feature interactions, providing a better approach to feature selection. This study proposes a new definition of feature relevancy and redundancy in PID terms and presents an iterative algorithm for feature selection based on conditional mutual information (CMI).
JOURNAL OF MACHINE LEARNING RESEARCH
(2023)
Article
Automation & Control Systems
Ping Zhang, Wanfu Gao, Juncheng Hu, Yonghao Li
Summary: Feature selection is crucial in machine learning and data mining, with traditional methods being improved upon by the novel CWJR-FS method, which utilizes conditional-weight joint relevance to design a new feature relevancy term, outperforming other methods in experiments.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(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, Information Systems
Heng Liu, Gregory Ditzler
Summary: In adversarial environments, feature selection (FS) in machine learning is vulnerable and has been largely overlooked in research; insecure FS in machine learning pipelines can be the Achilles heel of data pipelines.
INFORMATION SCIENCES
(2021)
Article
Engineering, Multidisciplinary
Sheng Bao, Li Zhang, Xueshan Han, Wensheng Li, Donglei Sun, Yijing Ren, Ningning Liu, Ming Yang, Boyi Zhang
Summary: A feature selection method that balances redundancy and relevancy is proposed in this study. By combining mutual information and CRITIC weight, redundancy elimination for the initial feature set is achieved, and a feature correlation ranking algorithm is established. The method shows promising results in improving identification accuracy with high robustness and generalization.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2022)
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
Keyu Liu, Tianrui Li, Xibei Yang, Hengrong Ju, Xin Yang, Dun Liu
Summary: Neighborhood granulation is a fundamental strategy for feature evaluation and selection, but it neglects observations across different levels of granularity. To address this issue, a novel algorithm called N3Y is proposed, which incorporates neighborhood relevancy, redundancy, and granularity interactivity. N3Y outperforms other feature selectors in extensive experiments.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Xi-Ao Ma, Hao Xu, Chunhua Ju
Summary: This paper proposes a class-specific feature selection method based on information theory. A class-specific feature evaluation criterion called CSMDCCMR is developed, and a feature selection algorithm is designed to select a suitable feature subset for each class. Experimental results demonstrate the superiority of the proposed method compared to other methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
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
Peng Wu, Qinghua Zhang, Guoyin Wang, Fan Yang, Fuzhong Xue
Summary: Feature selection is important for reducing dimensionality by selecting effective features from the original feature set. However, conventional methods often fail to accurately describe the correlations and dynamic changes between features, resulting in an incomplete evaluation function and compromised classification accuracy. This study proposes a dynamic feature selection method called DFS-SDII, which combines standard deviation and interaction information to address these challenges and improve feature selection performance.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Syed Fawad Hussain, Fatima Shahzadi, Badre Munir
Summary: Feature selection is an important step in preprocessing high-dimensional data for machine learning. Existing techniques often select features based on their maximum dependency with the category and minimum redundancy with already selected features, which can lead to biased classification towards specific classes. In this paper, we propose a novel approach based on information theory that selects features in a class-wise fashion and utilizes a constrained search to enhance the feature selection. Experimental results demonstrate the effectiveness of our method in improving accuracy and reducing time complexity compared to other state-of-the-art algorithms.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Software Engineering
Miron B. Kursa
Summary: Information filters are a significant class of feature selection methods that combine strong theoretical background, execution speed, and high selection quality. Praznik, an R package, gathers efficient implementations of these methods and showcases its functionality through illustrative example analyses on established benchmark datasets, highlighting competitive accuracy and very high computational efficiency compared to other tools. Additionally, Praznik exposes its low-level functionality to support the research and development of novel information theory-based methods.
Article
Computer Science, Artificial Intelligence
Jihong Wan, Hongmei Chen, Tianrui Li, Wei Huang, Min Li, Chuan Luo
Summary: Information theoretic-guided feature selection approaches aim to select the most informative features. However, previous approaches neglect the complementarity and interaction between features, and fail to fully consider the multi-correlation among features. This study addresses these issues by designing a feature selection algorithm based on class-based relevance, redundancy, complementarity, and interaction, and explores the distinctions and connections among different correlations.
PATTERN RECOGNITION
(2022)
Article
Automation & Control Systems
Shijie Zhao, Mengchen Wang, Shilin Ma, Qianqian Cui
Summary: Feature selection aims to improve classification accuracy by selecting crucial features in machine learning and data mining. This paper proposes a feature selection method called DSRFS, which uses a dynamic support ratio to consider the changing information supported by selected features and adaptively handles feature relevance and redundancy. Experimental results show that DSRFS effectively reduces the dimension of the feature space and achieves the best average classification accuracy.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Multidisciplinary
Jialu Gao, Jianzhou Wang, Danxiang Wei, He Jiang
Summary: This study proposes a feature selection strategy based on correlation and redundant feature judgment and introduces a novel combined interval prediction algorithm, the 3-Mcip system, to address the tradeoff between prediction accuracy and interval width. Numerical results demonstrate that the 3-Mcip system achieves coverage rates of 53.3333, 90.1667, and 99.4479 for Site 1 at different levels of interval width coefficients, providing both accurate power load prediction and analysis of uncertainty. This system is valuable for power system managers in capturing future load fluctuations and enhancing the flexibility of smart grid dispatching.
APPLIED MATHEMATICAL MODELLING
(2023)
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)