Classification of abdominal ECG recordings for the identification of fetal risk using random forest and optimal feature selection

2018 ◽  
Author(s):  
Fabian Torres ◽  
Boris Escalante-Ramirez ◽  
Jorge Perez-Gonzales ◽  
Roman Anselmo Mora-Gutierrrez ◽  
Antonin Ponsich ◽  
...  
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Fetal Risk ◽  
Optimal Feature Selection ◽  
Optimal Feature

Optimal feature selection for automated classification of FDG-PET in patients with suspected dementia

2009 ◽  
Author(s):  
Ahmed Serag ◽  
Fabian Wenzel ◽  
Frank Thiele ◽  
Ralph Buchert ◽  
Stewart Young
Keyword(s):  
Feature Selection ◽  
Fdg Pet ◽  
Automated Classification ◽  
Optimal Feature Selection ◽  
Selection For ◽  
Optimal Feature

scholarly journals Crop Classification Method Based on Optimal Feature Selection and Hybrid CNN-RF Networks for Multi-Temporal Remote Sensing Imagery

2020 ◽  
Vol 12 (19) ◽  
pp. 3119
Author(s):  
Shuting Yang ◽  
Lingjia Gu ◽  
Xiaofeng Li ◽  
Tao Jiang ◽  
Ruizhi Ren
Keyword(s):  
Remote Sensing ◽  
Feature Selection ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Multi Temporal ◽  
Optimal Feature Selection ◽  
Crop Classification ◽  
Optimal Feature

Although efforts and progress have been made in crop classification using optical remote sensing images, it is still necessary to make full use of the high spatial, temporal, and spectral resolutions of remote sensing images. However, with the increasing volume of remote sensing data, a key emerging issue in the field of crop classification is how to find useful information from massive data to balance classification accuracy and processing time. To address this challenge, we developed a novel crop classification method, combining optimal feature selection (OFSM) with hybrid convolutional neural network-random forest (CNN-RF) networks for multi-temporal optical remote sensing images. This research used 234 features including spectral, segmentation, color, and texture features from three scenes of Sentinel-2 images to identify crop types in the Jilin province of northeast China. To effectively extract the effective features of remote sensing data with lower time requirements, the use of OFSM was proposed with the results compared with two traditional feature selection methods (TFSM): random forest feature importance selection (RF-FI) and random forest recursive feature elimination (RF-RFE). Although the time required for OFSM was 26.05 s, which was between RF-FI with 1.97 s and RF-RFE with 132.54 s, OFSM outperformed RF-FI and RF-RFE in terms of the overall accuracy (OA) of crop classification by 4% and 0.3%, respectively. On the basis of obtaining effective feature information, to further improve the accuracy of crop classification we designed two hybrid CNN-RF networks to leverage the advantages of one-dimensional convolution (Conv1D) and Visual Geometry Group (VGG) with random forest (RF), respectively. Based on the selected optimal features using OFSM, four networks were tested for comparison: Conv1D-RF, VGG-RF, Conv1D, and VGG. Conv1D-RF achieved the highest OA at 94.27% as compared with VGG-RF (93.23%), Conv1D (92.59%), and VGG (91.89%), indicating that the Conv1D-RF method with optimal feature input provides an effective and efficient method of time series representation for multi-temporal crop-type classification.

Optimal feature selection using modified cuckoo search for classification of power quality disturbances

2021 ◽  
pp. 107897
Author(s):  
Ibrahim Mustafa Mehedi ◽  
Masoud Ahmadipour ◽  
Zainal Salam ◽  
Hussein Mohammed Ridha ◽  
Hussein Bassi ◽  
...  
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Cuckoo Search ◽  
Optimal Feature Selection ◽  
Power Quality Disturbances ◽  
Optimal Feature

Optimal Feature Selection Methods for Chronic Kidney Disease Classification using Intelligent Optimization Algorithms

2020 ◽  
Vol 13 ◽  
Author(s):  
Jerlin Rubini Lambert ◽  
Eswaran Perumal
Keyword(s):  
Chronic Kidney Disease ◽  
Feature Selection ◽  
Kidney Disease ◽  
Classification Performance ◽  
Disease Classification ◽  
Intelligent Optimization Algorithms ◽  
Optimal Feature Selection ◽  
Optimal Feature ◽  
New Framework

Aim: Recently, classification of medical data gives more importance to identify the existence of disease. Background: Numerous classification algorithms for chronic kidney disease (CKD) are developed and produced better classification results. But, the inclusion of different factors in the identification of CKD reduces the effectiveness of the employed classification algorithm. Objective: To overcome this issue, feature selection (FS) approaches are proposed to minimize the computational complexity and also to improve the classification performance in the identification of CKD. Since numerous bio-inspired based FS methodologies are developed, a need arises to examine the feature selection approaches performance of different algorithms on the identification of CKD. Method: This paper proposes a new framework for classification and prediction of CKD. Three feature selection approaches are used namely Ant Colony Optimization (ACO) algorithm, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the classification process of CKD. Finally, logistic regression (LR) classifier is employed for effective classification. Results: The effectiveness of the ACO-FS, GA-FS and PSO-FS are validated by testing it against a benchmark CKD dataset. Conclusion: The empirical results state that the ACO-FS algorithm performs well and the results reported that the classification performance is improved by the inclusion of feature selection methodologies in CKD classification.

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