A new improved maximal relevance and minimal redundancy method based on feature subset

Xie, Shanshan; Zhang, Yan; Lv, Danjv; Chen, Xu; Lü, Jing; Liu, Jiang

doi:10.1007/s11227-022-04763-2

Cited by 14 publications

(8 citation statements)

References 33 publications

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“…Let 𝑟 𝑖 𝑗 denote the ranking of the 𝑗 th (1 ≤ 𝑗 ≤ 𝑘) algorithm on the 𝑖th (1 ≤ 𝑖 ≤ 𝑁) dataset. The Friedman test compares the average ranking of algorithms, which the equation is shown in (11).…”

Section: A Comparison Of Fafs_hfs With Other Feature Selection Methodsmentioning

confidence: 99%

“…𝑅 𝑗 = 1 𝑁 ∑ 𝑟 𝑖 𝑗 𝑖 (11) Under the null hypothesis that all algorithms are equivalent and therefore their ranking 𝑅 𝑗 should be equal, equation (12) shows the distribution of the Friedman statistic.…”

Section: A Comparison Of Fafs_hfs With Other Feature Selection Methodsmentioning

confidence: 99%

“…In addition, Gonzalez-Lopez J, proposed Mutual Information Maximization (MIM) [9], Euclidean Norm Maximization (ENM) and Geometric Mean Maximization (GMM) [10] based on mutual information. Xie et al introduced an improved maximal relevance and minimal redundancy (ImRMR) feature selection method based on feature subset to reduce the dimension of sample features and time of model training and prediction, and improve the classification performance [11].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Feature Selection Method Based on Feature Subset and Factor Analysis

et al. 2022

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With the advent of big data era and the rapid improvement of raw data scale, feature selection is the basic and critical technologies for data mining. However, in most of the studies on feature selection methods before, mainly directed to treat the single feature or overall feature subset, while the influence of the correlation and redundancy of features in the feature subset on the classification results is ignored. In this paper, combination of feature subset grouping and factor analysis (FA), a hybrid feature selection method based on feature subsets generation through factor analysis (FAFS_HFS) is proposed. Firstly, generate feature subsets based on the maximum load (maximum explanatory power) of each feature through factor analysis. Then, minimum redundancy maximum relevance (mRMR) and sequential forward selection (SFS) is used to remove the redundancy of each feature subset. Finally, fisher score (F-score) and feature subset SFS (FS_SFS) was utilized to evaluate and select feature subset, and obtain the optimal feature subset. Experiments are conducted on 14 datasets, the results show that FAFS_HFS has high classification accuracy and dimension reduction on almost all datasets, especially in high-dimensional datasets, and it has excellent efficiency and competitive classification performance compared with other contrastive methods.INDEX TERMS hybrid feature selection, feature subset, factor analysis (FA), mRMR, F-score, sequential forward selection (SFS)

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Section: A Comparison Of Fafs_hfs With Other Feature Selection Methodsmentioning

confidence: 99%

Section: A Comparison Of Fafs_hfs With Other Feature Selection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Feature Selection Method Based on Feature Subset and Factor Analysis

et al. 2022

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“…Therefore, the features’ subset objective function is

with

where

represents the relevance between the sample feature set S and the sample class c , which is the mean of all mutual information between each feature

and class c , and

represents the redundancy of all features in S , which is the mean of all mutual information between feature

and feature

. We refer readers to [ 58 ] for more details about mRMR.…”

Section: System Descriptionmentioning

confidence: 99%

Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction

Tseng,

Wen

2023

Sensors

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This paper proposes a novel approach to tackle the human activity recognition (HAR) problem. Four classes of body movement datasets, namely stand-up, sit-down, run, and walk, are applied to perform HAR. Instead of using vision-based solutions, we address the HAR challenge by implementing a real-time HAR system architecture with a wearable inertial measurement unit (IMU) sensor, which aims to achieve networked sensing and data sampling of human activity, data pre-processing and feature analysis, data generation and correction, and activity classification using hybrid learning models. Referring to the experimental results, the proposed system selects the pre-trained eXtreme Gradient Boosting (XGBoost) model and the Convolutional Variational Autoencoder (CVAE) model as the classifier and generator, respectively, with 96.03% classification accuracy.

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“…Max-relevance-minimum-redundancy method 75 , 76 is based on the concept of MI and has been relevant in a number of previous studies. It deduces the target MI with minimum redundancy 10 , 77 among the selected features.…”

Section: Introductionmentioning

confidence: 99%

Hybrid similarity relation based mutual information for feature selection in intuitionistic fuzzy rough framework and its applications

Tiwari,

Saini,

Nath

et al. 2024

Sci Rep

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Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.

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A new improved maximal relevance and minimal redundancy method based on feature subset

Cited by 14 publications

References 33 publications

Hybrid Feature Selection Method Based on Feature Subset and Factor Analysis

Hybrid Feature Selection Method Based on Feature Subset and Factor Analysis

Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction

Hybrid similarity relation based mutual information for feature selection in intuitionistic fuzzy rough framework and its applications

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