scholarly journals A combinatorial cooperative-tabu search feature reduction approach

2013 ◽  
Author(s):  
E. Ansari ◽  
M.H. Sadreddini ◽  
B. Sadeghi Bigham ◽  
F. Alimardani
Keyword(s):  
Tabu Search ◽  
Feature Reduction ◽  
Search Feature ◽  
Reduction Approach

A Smart Feature Reduction Approach to Detect Botnet Attack in IoT

2021 ◽  
pp. 17-23
Author(s):  
Rup Kumar Deka ◽  
Kausthav Pratim Kalita ◽  
Dhruba Kumar Bhattacharyya ◽  
Debojit Boro
Keyword(s):  
Feature Reduction ◽  
Reduction Approach

scholarly journals Prediction of Hypertension Outcomes Based on Gain Sequence Forward Tabu Search Feature Selection and XGBoost

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 792
Author(s):  
Wenbing Chang ◽  
Xinpeng Ji ◽  
Yiyong Xiao ◽  
Yue Zhang ◽  
Bang Chen ◽  
...  
Keyword(s):  
Feature Selection ◽  
Tabu Search ◽  
Prediction Model ◽  
Medical Decision Making ◽  
Prediction Method ◽  
Medical Decision ◽  
Clinical Diagnoses ◽  
Search Feature ◽  
Auc Value ◽  
Fold Cross Validation

For patients with hypertension, serious complications, such as myocardial infarction, a common cause of heart failure, occurs in the late stage of hypertension. Hypertension outcomes can lead to complications, including death. Hypertension outcomes threaten patients’ lives and need to be predicted. In our research, we reviewed the hypertension medical data from a tertiary-grade A class hospital in Beijing, and established a hypertension outcome prediction model with the machine learning theory. We first proposed a gain sequence forward tabu search feature selection (GSFTS-FS) method, which can search the optimal combination of medical variables that affect hypertension outcomes. Based on this, the XGBoost algorithm established a prediction model because of its good stability. We verified the proposed method by comparing other commonly used models in similar works. The proposed GSFTS-FS improved the performance by about 10%. The proposed prediction method has the best performance and its AUC value, accuracy, F1 value, and recall of 10-fold cross-validation were 0.96. 0.95, 0.88, and 0.82, respectively. It also performed well on test datasets with 0.92, 0.94, 0.87, and 0.80 for AUC, accuracy, F1, and recall, respectively. Therefore, the XGBoost with GSFTS-FS can accurately and effectively predict the occurrence of outcomes for patients with hypertension, and can provide guidance for doctors in clinical diagnoses and medical decision-making.

scholarly journals KPIs-Based Clustering and Visualization of HPC jobs: a Feature Reduction Approach

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mohamed Soliman Halawa ◽  
Rebeca P. Diaz Redondo ◽  
Ana Fernandez Vilas
Keyword(s):  
Feature Reduction ◽  
Reduction Approach

scholarly journals Diagnosis methodology for identifying gearbox wear based on statistical time feature reduction

2017 ◽  
Vol 232 (15) ◽  
pp. 2711-2722 ◽  
Author(s):  
Juan Jose Saucedo-Dorantes ◽  
Miguel Delgado-Prieto ◽  
Roque Alfredo Osornio-Rios ◽  
Rene de Jesus Romero-Troncoso
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Condition Monitoring ◽  
High Performance ◽  
Industrial Applications ◽  
Feature Reduction ◽  
Data Set ◽  
Multi Stage ◽  
Statistical Time ◽  
Reduction Approach

Strategies for condition monitoring are relevant to improve the operation safety and to ensure the efficiency of all the equipment used in industrial applications. The feature selection and feature extraction are suitable processing stages considered in many condition monitoring schemes to obtain high performance. Aiming to address this issue, this work proposes a new diagnosis methodology based on a multi-stage feature reduction approach for identifying different levels of uniform wear in a gearbox. The proposed multi-stage feature reduction approach involves a feature selection and a feature extraction ensuring the proper application of a high-performance signal processing over a set of acquired measurements of vibration. The methodology is performed successively; first, the acquired vibration signals are characterized by calculating a set of statistical time-based features. Second, a feature selection is done by performing an analysis of the Fisher score. Third, a feature extraction is realized by means of the linear discriminant analysis technique. Finally, fourth, the diagnosis of the considered faults is done by means of a fuzzy-based classifier. The effectiveness and performance of the proposed diagnosis methodology are evaluated by considering a complete data set of experimental test, making the proposed methodology suitable to be applied in industrial applications with power transmission systems.

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