Article
Computer Science, Artificial Intelligence
Tomasz Klonecki, Pawel Teisseyre, Jaesung Lee
Summary: Feature selection is crucial in multi-label classification for building predictive models. Existing methods often disregard cost information associated with considered features. We address the problem of cost-constrained multilabel feature selection, aiming to select a feature subset relevant to multiple labels while adhering to a user-defined budget. Our approach ensures high predictive power without exceeding the specified budget per prediction. We propose a novel criterion combining relevance and cost for feature selection, along with an effective method for determining the trade-off between relevancy and cost. Experimental results demonstrate the superiority of our method over conventional methods on multilabel datasets.
PATTERN RECOGNITION
(2023)
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
Jinghua Liu, Yaojin Lin, Weiping Ding, Hongbo Zhang, Jixiang Du
Summary: Multilabel feature selection (MFS) is an important topic in big data applications. However, current methods often assume that all labels are given in advance or that they are independent. This article proposes a novel approach called MSDS, which can handle single streaming label, minibatch streaming labels, and label dependency simultaneously. The method uses fuzzy mutual information to measure label relationships, and combines label dependency and streaming labels to analyze feature relevance and redundancy. Experimental results show the superiority of MSDS compared to other algorithms.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Physics, Multidisciplinary
Lei Zan, Anouar Meynaoui, Charles K. Assaad, Emilie Devijver, Eric Gaussier
Summary: In this study, we focus on mixed data and propose a novel method, CMIh, to estimate conditional mutual information. We also introduce a new local permutation test, LocAT, which is well suited for mixed data. Our experiments demonstrate the good performance of CMIh and LocAT in accurately estimating conditional mutual information and detecting conditional (in)dependence for mixed data.
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
Lin Sun, Yusheng Chen, Weiping Ding, Jiucheng Xu, Yuanyuan Ma
Summary: This article proposes a novel adaptive fuzzy neighborhood-based multilabel feature subset selection approach with ant colony optimization (ACO) for multilabel classification. It addresses the issue of ignoring correlations among labels and the manual setting of neighborhood radius in existing feature selection models. The approach combines feature cosine similarity and label Jaccard similarity to effectively reflect overall similarity between samples, and utilizes dynamic adjustment coefficients to control label similarity importance. Experimental results demonstrate the effectiveness of the proposed algorithm in achieving excellent feature subset for multilabel classification.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Ahmad Esfandiari, Hamid Khaloozadeh, Faezeh Farivar
Summary: This paper introduces a multivariate filter feature selection method called interaction-based feature clustering (IFC), which is cost-effective in terms of computational cost while achieving high classification accuracy. The proposed method ranks features based on the symmetric uncertainty criterion and performs feature clustering by calculating their interactive weight as a similarity measure. Experimental results show that the IFC algorithm is more efficient than comparable methods in terms of classification accuracy and computational time.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(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
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
Xiangyuan Gu, Jichang Guo, Lijun Xiao, Chongyi Li
Summary: This paper investigates feature selection based on the three-dimensional mutual information among features, proposing a CMI-MRMR algorithm based on conditional mutual information for maximal relevance minimal redundancy.
The algorithm describes relevance and redundancy using joint mutual information among class label and feature set, as well as mutual information between feature sets, showing better feature selection performance in experimental results.
APPLIED INTELLIGENCE
(2022)
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
Computer Science, Artificial Intelligence
Tingting Pan, Witold Pedrycz, Jie Yang, Wei Wu, Yulin Zhang
Summary: In this paper, a new iterative ensemble classifier (C-ILEO) is proposed for imbalanced data. The iterative learning process and ensemble operating process are used to improve classification performance by selecting a small number of features and optimizing class weights. Experimental results show that C-ILEO outperforms other algorithms and methods on imbalanced datasets.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Wanfu Gao, Yonghao Li, Liang Hu
Summary: When dealing with high-dimensional multilabel data, we propose a feature selection method that shares latent feature and label structure. By designing an LSS term to share and preserve the latent structure, and employing graph regularization technique to ensure consistency, we achieve better results on multiple evaluation criteria according to experiments.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Gaoteng Yuan, Yi Zhai, Jiansong Tang, Xiaofeng Zhou
Summary: This paper proposes a feature selection algorithm based on cosine similarity coefficient and information measurement criterion (CSCIM_FS). The algorithm calculates the mutual information (MI) between features and tags, and sorts the features according to the calculated MI. It constructs a feature matrix to transform the one-dimensional feature sequence into a two-dimensional square matrix. The experimental results show that the CSCIM_FS algorithm selected a feature subset with high accuracy and outperforms other algorithms.
Article
Biochemistry & Molecular Biology
Shida He, Xiucai Ye, Tetsuya Sakurai, Quan Zou
Summary: In this study, we developed a dimensionality reduction tool, MRMD3.0, based on the ensemble strategy of link analysis. The tool integrates different feature ranking algorithms to calculate feature importance and uses forward feature search strategy combined with cross-validation to explore proper feature combinations. The latest version added more link-based ensemble algorithms and improved the speed and effects of feature ranking algorithms, providing an interface and charts for feature analysis. An online webserver is also available to assist researchers in data analysis.
JOURNAL OF MOLECULAR BIOLOGY
(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)