Weighted ReliefF with threshold constraints of feature selection for imbalanced data classification

Song, Yan; Si, Weiyun; Dai, Feifan; Yang, Guisong

doi:10.1002/cpe.5691

Cited by 13 publications

(7 citation statements)

References 38 publications

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“…It is capable of detecting feature dependencies by indirectly deriving interactions through the concept of nearest neighbors [ 64 ]. Furthermore, Relief-F is a non-parametric feature selection method, which allows it to determine feature importance across a wide range of datasets without relying on the underlying distribution of the data [ 65 ]. Contrary to other filter-based feature selection methods, Relief-F has more robustness against imbalanced datasets [ 65 ].…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, Relief-F is a non-parametric feature selection method, which allows it to determine feature importance across a wide range of datasets without relying on the underlying distribution of the data [ 65 ]. Contrary to other filter-based feature selection methods, Relief-F has more robustness against imbalanced datasets [ 65 ]. Thus, it has been preferred for our imbalanced dataset.…”

Section: Methodsmentioning

confidence: 99%

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Comparative Analysis of Fall Risk Assessment Features in Community-Elderly and Stroke Survivors: Insights from Sensor-Based Data

et al. 2023

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Fall-risk assessment studies generally focus on identifying characteristics that affect postural balance in a specific group of subjects. However, falls affect a multitude of individuals. Among the groups with the most recurrent fallers are the community-dwelling elderly and stroke survivors. Thus, this study focuses on identifying a set of features that can explain fall risk for these two groups of subjects. Sixty-five community dwelling elderly (forty-nine female, sixteen male) and thirty-five stroke-survivors (twenty-two male, thirteen male) participated in our study. With the use of an inertial sensor, some features are extracted from the acceleration data of a Timed Up and Go (TUG) test performed by both groups of individuals. A short-form berg balance scale (SFBBS) score and the TUG test score were used for labeling the data. With the use of a 100-fold cross-validation approach, Relief-F and Extra Trees Classifier algorithms were used to extract sets of the top 5, 10, 15, 20, 25, and 30 features. Random Forest classifiers were trained for each set of features. The best models were selected, and the repeated features for each group of subjects were analyzed and discussed. The results show that only the stand duration was an important feature for the prediction of fall risk across all clinical tests and both groups of individuals.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Fall Risk Assessment Features in Community-Elderly and Stroke Survivors: Insights from Sensor-Based Data

et al. 2023

View full text Add to dashboard Cite

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“…The most important step in the ReliefF algorithm is after calculating the diff function, the estimation of weight for each feature, which is [ 124 ]

…”

Section: Methodsmentioning

confidence: 99%

Cuffless Blood Pressure Measurement Using Linear and Nonlinear Optimized Feature Selection

Mamun

Alouani

2022

Diagnostics

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The cuffless blood pressure (BP) measurement allows for frequent measurement without discomfort to the patient compared to the cuff inflation measurement. With the availability of a large dataset containing physiological waveforms, now it is possible to use them through different learning algorithms to produce a relationship with changes in BP. In this paper, a novel cuffless noninvasive blood pressure measurement technique has been proposed using optimized features from electrocardiogram and photoplethysmography based on multivariate symmetric uncertainty (MSU). The technique is an improvement over other contemporary methods due to the inclusion of feature optimization depending on both linear and nonlinear relationships with the change of blood pressure. MSU has been used as a selection criterion with algorithms such as the fast correlation and ReliefF algorithms followed by the penalty-based regression technique to make sure the features have maximum relevance as well as minimum redundancy. The result from the technique was compared with the performance of similar techniques using the MIMIC-II dataset. After training and testing, the root mean square error (RMSE) comes as 5.28 mmHg for systolic BP and 5.98 mmHg for diastolic BP. In addition, in terms of mean absolute error, the result improved to 4.27 mmHg for SBP and 5.01 for DBP compared to recent cuffless BP measurement techniques which have used substantially large datasets and feature optimization. According to the British Hypertension Society Standard (BHS), our proposed technique achieved at least grade B in all cumulative criteria for cuffless BP measurement.

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“…In this paper, two commonly used feature evaluation methods, Laplacian Score (LS) [29] and Relief-F Score (RFS) [30], are used to evaluate the effectiveness of the timedomain and frequency-domain features of the satellite momentum wheel telemetry signal.…”

Section: Feature Evaluation and Selectionmentioning

confidence: 99%

A Novel Framework for Anomaly Detection for Satellite Momentum Wheel Based on Optimized SVM and Huffman-Multi-Scale Entropy

Lei

Liu³

et al. 2021

Entropy

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The health status of the momentum wheel is vital for a satellite. Recently, research on anomaly detection for satellites has become more and more extensive. Previous research mostly required simulation models for key components. However, the physical models are difficult to construct, and the simulation data does not match the telemetry data in engineering applications. To overcome the above problem, this paper proposes a new anomaly detection framework based on real telemetry data. First, the time-domain and frequency-domain features of the preprocessed telemetry signal are calculated, and the effective features are selected through evaluation. Second, a new Huffman-multi-scale entropy (HMSE) system is proposed, which can effectively improve the discrimination between different data types. Third, this paper adopts a multi-class SVM model based on the directed acyclic graph (DAG) principle and proposes an improved adaptive particle swarm optimization (APSO) method to train the SVM model. The proposed method is applied to anomaly detection for satellite momentum wheel voltage telemetry data. The recognition accuracy and detection rate of the method proposed in this paper can reach 99.60% and 99.87%. Compared with other methods, the proposed method can effectively improve the recognition accuracy and detection rate, and it can also effectively reduce the false alarm rate and the missed alarm rate.

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Weighted ReliefF with threshold constraints of feature selection for imbalanced data classification

Cited by 13 publications

References 38 publications

Comparative Analysis of Fall Risk Assessment Features in Community-Elderly and Stroke Survivors: Insights from Sensor-Based Data

Comparative Analysis of Fall Risk Assessment Features in Community-Elderly and Stroke Survivors: Insights from Sensor-Based Data

Cuffless Blood Pressure Measurement Using Linear and Nonlinear Optimized Feature Selection

A Novel Framework for Anomaly Detection for Satellite Momentum Wheel Based on Optimized SVM and Huffman-Multi-Scale Entropy

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