Article
Automation & Control Systems
Yadi Wang, Xiaoping Li, Ruben Ruiz
Summary: Feature selection is crucial for classification of large-scale images and bio-microarray data. This article proposes an efficient FS algorithm that considers the relevance of features and their correlation with class labels to improve classification accuracy. Experimental results demonstrate that the proposed method is effective in selecting informative features and achieves better classification performance.
IEEE TRANSACTIONS ON CYBERNETICS
(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, Artificial Intelligence
Kexin Yin, Aifeng Xie, Junren Zhai, Jianqi Zhu
Summary: This paper proposes an algorithm called DIMRMR to solve the problem of confusing redundancy and dependency in feature selection. By introducing the concept of feature interaction degree and defining new discriminant criteria, the ability to distinguish is further improved. Experimental results show that the proposed algorithm can achieve optimal classification performance on most datasets.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Keyu Liu, Tianrui Li, Xibei Yang, Hongmei Chen, Jie Wang, Zhixuan Deng
Summary: In this article, a novel semi-supervised feature selection scheme called SemiFREE is proposed, which redefines the feature relevance and redundancy by considering the fuzziness or uncertainty in data labeling. Experimental results demonstrate the superiority of SemiFREE in the presence of partially labeled data.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Lianxi Wang, Shengyi Jiang, Siyu Jiang
Summary: The study introduces a novel feature selection algorithm that selects relevant and interactive features using a maximum criterion, leading to improved classification accuracy. Experimental results show that the algorithm efficiently selects features and enhances classifiers to achieve better or comparable classification accuracy compared to ten representative competing feature selection algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Xi-Ao Ma, Chunhua Ju
Summary: The concepts of feature relevance, redundancy, and complementarity are investigated based on fuzzy information-theoretic measures to handle continuous features. Theoretical definitions and a computationally effective feature evaluation criterion are proposed, and a feature selection algorithm combining the criterion with the sequential forward search strategy is introduced. Experimental results demonstrate the effectiveness of the method.
INFORMATION SCIENCES
(2022)
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, Information Systems
Wenbin Qian, Qianzhi Ye, Yihui Li, Jintao Huang, Shiming Dai
Summary: This paper proposes a relevance-based label distribution feature selection method, aiming to reduce model complexity and computational cost, as well as enhance the generalization ability of the learning model. By considering the relevance between features and labels, a convex optimization function is used for feature selection, and the Pearson correlation coefficient is used to describe the correlation information between labels. Experimental results demonstrate the effectiveness of the proposed method compared to existing methods in multiple evaluation metrics.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Hardware & Architecture
Sadegh Eskandari
Summary: Multi-label learning deals with data in which an instance may belong to multiple class labels simultaneously. The main problem of the existing multi-label feature selection algorithms is their inability to consider all possible subsets of feature space. This paper proposes a new method to address higher-order relevance and redundancy analysis and experimental results demonstrate its superiority over seven state-of-the-art multi-label feature selection algorithms.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Physics, Multidisciplinary
Lingbo Gao, Yiqiang Wang, Yonghao Li, Ping Zhang, Liang Hu
Summary: A novel feature selection method, combining three aspects of candidate features, selected features, and label correlations, is proposed in this paper to evaluate feature relevance. By reducing unnecessary redundancy, the method is able to better capture the optimal features and outperform other state-of-the-art multi-label approaches in experiments.
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
G. Manikandan, S. Abirami
Summary: Feature selection is essential in pattern recognition and bioinformatics, as high-dimensional datasets often contain redundant and irrelevant features. The proposed MIMCFS technique effectively selects important features and eliminates redundancies through two stages. Experimental results show superior performance compared to existing methods.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Lin Sun, Tengyu Yin, Weiping Ding, Yuhua Qian, Jiucheng Xu
Summary: This article presents a feature selection method based on multilabel fuzzy neighborhood rough sets and maximum relevance minimum redundancy for multilabel data with missing labels. Experiments verify the effectiveness of the method in recovering missing labels and selecting significant features.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jia Zhang, Yidong Lin, Min Jiang, Shaozi Li, Yong Tang, Jinyi Long, Jian Weng, Kay Chen Tan
Summary: In this article, the authors propose a general optimization framework called global relevance and redundancy optimization (GRRO) for multilabel feature selection (MLFS). They also extend the framework to improve efficiency. The proposed algorithms show significant advantages in terms of effectiveness and efficiency.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
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)
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Brian J. d'Auriol
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(2016)
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Brian Joseph d'Auriol
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Brian J. d'Auriol
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(2017)
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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2009)
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Engineering, Electrical & Electronic
Uzair Ahmad, Brian J. D'Auriol, Young-Koo Lee, Sungyoung Lee
IEICE TRANSACTIONS ON COMMUNICATIONS
(2008)
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Computer Science, Theory & Methods
Brian J. d'Auriol, Juan Rene Roldan
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(2009)
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Computer Science, Hardware & Architecture
Brian J. d'Auriol
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(2009)
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Yu Niu, Brian J. d'Auriol, Sungyoung Lee
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(2012)
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Brian J. d'Auriol
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(2012)
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Computer Science, Software Engineering
Ilya Malyanov, Brian J. d'Auriol, Sungyoung Lee
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(2013)
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Chemistry, Analytical
Riaz Ahmed Shaikh, Hassan Jameel, Brian J. d'Auriol, Heejo Lee, Sungyoung Lee, Young-Jae Song
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Chemistry, Analytical
Riaz Ahmed Shaikh, Hassan Jameel, Brian J. d'Auriol, Heejo Lee, Sungyoung Lee, Young-Jae Song
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Computer Science, Software Engineering
Brian J. d'Auriol
INFORMATION VISUALIZATION
(2020)