Article
Computer Science, Artificial Intelligence
Arpita Chaudhuri, Debasis Samanta, Monalisa Sarma
Summary: This paper discusses the basic methods of unsupervised feature selection and proposes a UFS scheme suitable for mixed datasets. The proposed two-phase process results in a better subset of features.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Pawel Teisseyre, Jaesung Lee
Summary: In this paper, a multilabel all-relevant feature selection task is discussed for multi-label classification. The all-relevant methods aim to identify all features related to target labels, including weakly relevant features. The paper proposes a relevancy score calculation method based on conditional mutual information and a testing procedure for separating relevant and irrelevant features.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
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
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
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
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
Engineering, Electrical & Electronic
Magda Amiridi, Nikos Kargas, Nicholas D. Sidiropoulos
Summary: This paper introduces a method of feature selection through low-rank tensor modeling to mitigate complexity and maximize classification performance. By learning the "principal components" of the joint distribution to avoid the curse of dimensionality, and by using a greedy algorithm to tackle the feature selection problem.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
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
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
Pei Huang, Xiaowei Yang
Summary: Unsupervised feature selection is an important topic in the fields of machine learning, pattern recognition and data mining. A novel method called AGDS is proposed to address the issues in feature selection by utilizing adaptive graph and dependency score.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Snehalika Lall, Debajyoti Sinha, Abhik Ghosh, Debarka Sengupta, Sanghamitra Bandyopadhyay
Summary: The study introduces a feature selection algorithm based on copula that maximizes feature relevance and minimizes redundant information. The proposed CBFS algorithm competes well in maximizing classification accuracy on real and synthetic datasets and demonstrates better noise tolerance compared to other methods.
PATTERN RECOGNITION
(2021)
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
Utkarsh Agrawal, Vasudha Rohatgi, Rahul Katarya
Summary: The problem of feature selection involves selecting the most informative subset of features that have the most impact in classification. This paper proposes a novel variant of the Equilibrium Optimizer called Normalized Mutual Information-based equilibrium optimizer (NMIEO) for feature selection. The proposed method incorporates a local search strategy based on Normalized Mutual Information and utilizes Chaotic maps for population initialization. Experimental results demonstrate the superior performance of NMIEO compared to other competitive methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
