Review
Physics, Multidisciplinary
Jan Mielniczuk
Summary: This paper reviews the information theoretic tools and their application in feature selection, focusing on classification problems with discrete features. The authors discuss various ways of constructing counterparts to conditional mutual information and their properties and limitations. They propose a unified method based on truncation for the Mobius expansion of conditional mutual information. The paper also discusses the main approaches to feature selection using the introduced measures of conditional dependence, along with methods for assessing the quality of the obtained predictors, including recent results on asymptotic distributions of empirical criteria and advances in resampling.
Article
Computer Science, Artificial Intelligence
David S. Watson, Marvin N. Wright
Summary: The proposed method introduces a new estimator called Conditional Predictive Impact (CPI) for measuring the association between features and outcomes under reduced feature sets. Through various algorithm tests and simulations, it has been demonstrated that CPI performs favorably compared to alternative methods.
Article
Multidisciplinary Sciences
Waqar Khan, Lingfu Kong, Brekhna Brekhna, Ling Wang, Huigui Yan
Summary: This paper proposes an online feature selection algorithm called OFSVMB, which is based on statistical conditional independence tests. It significantly improves accuracy and computation time by reducing the number of tests and incorporating online relevance and redundant analysis. Experimental results show that OFSVMB outperforms traditional algorithms and other streaming feature selection algorithms in terms of accuracy and efficiency on various datasets.
Article
Computer Science, Information Systems
Xianjie Guo, Kui Yu, Fuyuan Cao, Peipei Li, Hao Wang
Summary: Causal feature selection has gained much attention in recent years due to its improved robustness compared to traditional feature selection methods. However, existing algorithms that rely on conditional independence tests often encounter errors in practice, leading to degraded performance. In this paper, we propose an Error-Aware Markov Blanket learning algorithm with novel subroutines to address this issue, achieving better performance compared to state-of-the-art causal feature selection algorithms and traditional feature selection methods.
INFORMATION SCIENCES
(2022)
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
Niantai Wang, Haoran Liu, Liyue Zhang, Yanbin Cai, Qianrui Shi
Summary: In this paper, a novel divide-and-conquer discovery algorithm LSMB is proposed for more efficient and accurate Markov blanket (MB) discovery. The algorithm combines loose and strict conditional independence (CI) tests to discover the approximate parent-child and spouse sets of a target variable, and then removes non-MB nodes using strict CI tests. Experimental results on benchmark and real-world datasets demonstrate the superior performance of LSMB in MB discovery and feature selection.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Shalev Shaer, Yaniv Romano
Summary: The model-X conditional randomization test is a versatile method for testing conditional independence and controlling type I errors. By introducing novel model-fitting schemes with a new cost function, we aim to improve the power of the test statistic used to measure violations of conditional independence. Our experiments show that this approach consistently increases the number of correct discoveries while maintaining control over type I errors.
Article
Computer Science, Artificial Intelligence
Xiao-Lin Xu, Geng-Xin Xu, Chuan-Xian Ren, Dao-Qing Dai, Hong Yan
Summary: In this paper, a conditional independence induced unsupervised domain adaptation (CIDA) method is proposed to tackle the dataset bias problem. The method aims to find the low-dimensional and transferable feature representation by optimizing mutual information terms. Experimental results demonstrate the effectiveness of CIDA.
PATTERN RECOGNITION
(2023)
Article
Economics
Hakon Otneim, Dag Tjostheim
Summary: This article introduces a new measure of conditional dependence called the local Gaussian partial correlation (LGPC). Compared to traditional partial correlation coefficients, LGPC can better describe conditional dependence in a wide range of populations and has some useful and novel properties. LGPC can also be used to study departures from conditional independence in specific parts of the distribution.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2022)
Article
Physics, Multidisciplinary
Christos Meselidis, Alex Karagrigoriou
Summary: This work focuses on a general family of measures of divergence, emphasizing conditional independence in cross tabulations for estimation and testing purposes. The study utilizes a restricted minimum divergence estimator to estimate parameters under constraints and introduces a new double index divergence test statistic, which is thoroughly examined. The associated asymptotic theory is provided, and the advantages and practical implications are explored through simulation studies.
Article
Physics, Multidisciplinary
Camil Bancioiu, Remus Brad
Summary: This article introduces a novel and efficient method of computing the statistical G-test by decomposing it into easily reusable partial results and demonstrates outstanding efficiency gains in applications such as feature selection and causal inference.
Article
Automation & Control Systems
Lasse Petersen, Niels Richard Hansen
Summary: This paper develops a nonparametric test for conditional independence by combining the partial copula with a quantile regression based method for estimating the nonparametric residuals. The resulting test is demonstrated to be sound under complicated data generating distributions and competitive to other state-of-the-art conditional independence tests, with superior power in cases with conditional variance heterogeneity of X and Y given Z.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Article
Physics, Multidisciplinary
Dewei Ma, Weijie Ren, Min Han
Summary: This study proposes a two-stage causal network learning method to reveal causalities between variables and construct an accurate prediction model. The method includes a feature selection stage and a conditional independence test stage. Experimental results show that the proposed method can effectively analyze causalities and construct accurate prediction models.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Junghye Lee, In Young Choi, Chi-Hyuck Jun
Summary: Classification of microarray data is crucial for cancer diagnosis and prediction, but the high dimensionality could pose challenges.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Biology
Tamas Spisak
Summary: The proposed partial confounder test provides a strict control for type I errors and high statistical power, even in the presence of nonnormally and nonlinearly dependent predictions. It can reveal previously unreported confounders and identify cases where state-of-the-art confound mitigation approaches fail.
Article
Computer Science, Artificial Intelligence
Suhyeon Kim, Haecheong Park, Junghye Lee
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
Junghye Lee, In Young Choi, Chi-Hyuck Jun
Summary: Classification of microarray data is crucial for cancer diagnosis and prediction, but the high dimensionality could pose challenges.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Business
MyoungHoon Lee, Suhyeon Kim, Hangyeol Kim, Junghye Lee
Summary: To capture emerging technologies in the fast-changing technology market, this study proposes a new technology opportunity discovery framework that uses text mining and a knowledge graph to exploit the information from technology, new technology-based firms (NTBFs), and investors. Empirical results demonstrate the accuracy and validity of the framework.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2022)
Article
Medicine, General & Internal
Seok-Ju Hahn, Suhyeon Kim, Young Sik Choi, Junghye Lee, Jihun Kang
Summary: Previous studies on predicting type 2 diabetes risk by integrating clinical and genetic factors have mainly focused on the Western population. This study used genome-wide polygenic risk score (gPRS) and serum metabolite data to predict type 2 diabetes risk in the Asian population. The results showed that incorporating both gPRS and metabolite data led to more accurate prediction of type 2 diabetes risk.
Article
Business, Finance
Kyeongbin Kim, Yoontae Hwang, Dongcheol Lim, Suhyeon Kim, Junghye Lee, Yongjae Lee
Summary: This study proposes a new data-driven framework for the financial health of households, utilizing a deep learning-based diagnostic model to estimate financial health risk scores and provide recommendations for improvement.
QUANTITATIVE FINANCE
(2023)
Article
Computer Science, Information Systems
Taek-Ho Lee, Suhyeon Kim, Junghye Lee, Chi-Hyuck Jun
Summary: This study proposes an extension of Word2Vec for a privacy-preserving federated sequential recommendation system, which utilizes sequential information to generate contextual item representations for accurate recommendations while concealing privacy-sensitive features. Experiment results show that the proposed method has little degradation in recommendation performance compared to non-privacy-preserving methods.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Theory & Methods
Miran Kim, Junghye Lee, Lucila Ohno-Machado, Xiaoqian Jiang
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2020)
Article
Business
Junghye Lee, Ryeok-Hwan Kwon, Hyung Woo Kim, Sung-Hong Kang, Kwang-Jae Kim, Chi-Hyuck Jun
Article
Medical Informatics
Yingxiang Huang, Junghye Lee, Shuang Wang, Jimeng Sun, Hongfang Liu, Xiaoqian Jiang
JMIR MEDICAL INFORMATICS
(2018)
Article
Medical Informatics
Junghye Lee, Jimeng Sun, Fei Wang, Shuang Wang, Chi-Hyuck Jun, Xiaoqian Jiang
JMIR MEDICAL INFORMATICS
(2018)
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)