scholarly journals An Efficient Chaotic Gradient-Based Optimizer for Feature Selection

IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Diaa Salama Abd Elminaam ◽  
Shimaa Abdallah Ibrahim ◽  
Essam H. Houssein ◽  
Salah M Elsayed
Keyword(s):  
Feature Selection ◽  
Gradient Based

Enhanced feature selection models using gradient-based and point injection techniques

2008 ◽  
Vol 71 (16-18) ◽  
pp. 3114-3123 ◽  
Author(s):  
D. Huang ◽  
Zhaohui Gan ◽  
Tommy W.S. Chow
Keyword(s):  
Feature Selection ◽  
Selection Models ◽  
Point Injection ◽  
Gradient Based ◽  
Injection Techniques

A Fully Automated Breast Cancer Recognition System Using Discrete-Gradient Based Clustering and Multi Category Feature Selection

2005 ◽  
Vol 9 (3) ◽  
pp. 244-256
Author(s):  
Ranadhir Ghosh ◽  
◽  
Moumita Ghosh ◽  
John Yearwood
Keyword(s):  
Feature Selection ◽  
Diagnostic System ◽  
Recognition System ◽  
Machine Intelligence ◽  
Automated System ◽  
Cad System ◽  
Gradient Based ◽  
Feature Selection Approach ◽  
Mlp Model ◽  
Category Feature

Advances in machine intelligence have provided a whole new window of opportunities in medical research. Building a fully automated computer aided diagnostic system for digital mammograms is just one of them. Given some success with semi-automated systems earlier, a fully automated CAD system is just another step forward. A proper combination of a feature selection model and a classifier for those areas of a mammogram marked by radiologists has been very successful. However a fully automated system with only two modules is a time consuming process as the suspicious areas in a mammogram can be quite small when compared to the whole image. Thus an additional clustering process can help in reducing the time complexity of the overall process. In this paper we propose a fast clustering process to identify suspicious areas. Another novelty of this paper is a multi-category feature selection approach. The choice of features to represent the patterns affects several aspects of pattern recognition problems such as accuracy, required learning time and the required number of samples. In this paper we propose a hybrid canonical based feature extraction technique as a combination of an evolutionary algorithm based classifier with a feed forward MLP model.

scholarly journals Gradient-Based Multi-Objective Feature Selection for Gait Mode Recognition of Transfemoral Amputees

2018 ◽  
Author(s):  
Gholamreza Khademi ◽  
Hanieh Mohammadi ◽  
Dan Simon
Keyword(s):  
Feature Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
User Intent ◽  
Multi Objective ◽  
Mode Recognition ◽  
Gradient Based ◽  
Transfemoral Amputees ◽  
Optimal Feature Subset ◽  
Optimal Feature

One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultaneous maximum performance and parsimony for gait mode recognition. We use multi-objective optimization (MOO) to find an optimal feature subset that creates a trade-off between these two conflicting objectives. The main contribution of this paper is two-fold: (1) a new gradient-based multi-objective feature selection (GMOFS) method for optimal UIR design; and (2) the application of advanced evolutionary MOO methods for UIR. GMOFS is an embedded method that simultaneously performs feature selection and classification by incorporating an elastic net in multilayer perceptron neural network training. Experimental data are collected from six subjects, including three able-bodied subjects and three transfemoral amputees. We implement GMOFS and four variants of multi-objective biogeography-based optimization (MOBBO) for optimal feature subset selection, and we compare their performances using normalized hypervolume and relative coverage. GMOFS demonstrates competitive performance compared to the four MOBBO methods. We achieve a mean classification accuracy of 97.14% ± 1.51% and 98.45% ± 1.22% with the optimal selected subset for able-bodied and amputee subjects, respectively, while using only 23% of the available features. Results thus indicate the potential of advanced optimization methods to simultaneously achieve accurate, reliable, and compact UIR for locomotion mode detection of lower-limb amputees with prostheses.

scholarly journals A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1065
Author(s):  
Ahmed Mohamed Helmi ◽  
Mohammed A. A. Al-qaness ◽  
Abdelghani Dahou ◽  
Robertas Damaševičius ◽  
Tomas Krilavičius  ◽  
...  
Keyword(s):  
Feature Selection ◽  
Activity Recognition ◽  
Human Activity ◽  
Elderly Care ◽  
Feature Selection Method ◽  
Human Activity Recognition ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Gradient Based

Human activity recognition (HAR) plays a vital role in different real-world applications such as in tracking elderly activities for elderly care services, in assisted living environments, smart home interactions, healthcare monitoring applications, electronic games, and various human–computer interaction (HCI) applications, and is an essential part of the Internet of Healthcare Things (IoHT) services. However, the high dimensionality of the collected data from these applications has the largest influence on the quality of the HAR model. Therefore, in this paper, we propose an efficient HAR system using a lightweight feature selection (FS) method to enhance the HAR classification process. The developed FS method, called GBOGWO, aims to improve the performance of the Gradient-based optimizer (GBO) algorithm by using the operators of the grey wolf optimizer (GWO). First, GBOGWO is used to select the appropriate features; then, the support vector machine (SVM) is used to classify the activities. To assess the performance of GBOGWO, extensive experiments using well-known UCI-HAR and WISDM datasets were conducted. Overall outcomes show that GBOGWO improved the classification accuracy with an average accuracy of 98%.

scholarly journals Gradient-Based Multi-Objective Feature Selection for Gait Mode Recognition of Transfemoral Amputees

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 253 ◽  
Author(s):  
Gholamreza Khademi ◽  
Hanieh Mohammadi ◽  
Dan Simon
Keyword(s):  
Feature Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
User Intent ◽  
Multi Objective ◽  
Mode Recognition ◽  
Gradient Based ◽  
Transfemoral Amputees ◽  
Optimal Feature Subset ◽  
Optimal Feature

One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultaneous maximum performance and minimum complexity for gait mode recognition. We use multi-objective optimization (MOO) to find an optimal feature subset that creates a trade-off between these two conflicting objectives. The main contribution of this paper is two-fold: (1) a new gradient-based multi-objective feature selection (GMOFS) method for optimal UIR design; and (2) the application of advanced evolutionary MOO methods for UIR. GMOFS is an embedded method that simultaneously performs feature selection and classification by incorporating an elastic net in multilayer perceptron neural network training. Experimental data are collected from six subjects, including three able-bodied subjects and three transfemoral amputees. We implement GMOFS and four variants of multi-objective biogeography-based optimization (MOBBO) for optimal feature subset selection, and we compare their performances using normalized hypervolume and relative coverage. GMOFS demonstrates competitive performance compared to the four MOBBO methods. We achieve a mean classification accuracy of 97.14 % ± 1.51 % and 98.45 % ± 1.22 % with the optimal selected subset for able-bodied and amputee subjects, respectively, while using only 23% of the available features. Results thus indicate the potential of advanced optimization methods to simultaneously achieve accurate, reliable, and compact UIR for locomotion mode detection of lower-limb amputees with prostheses.

scholarly journals An efficient binary Gradient-based optimizer for feature selection

2021 ◽  
Vol 18 (4) ◽  
pp. 3813-3854
Author(s):  
Yugui Jiang ◽  
◽  
Qifang Luo ◽  
Yuanfei Wei ◽  
Laith Abualigah ◽  
...  
Keyword(s):  
Feature Selection ◽  
Gradient Based
Sign in / Sign up

Export Citation Format

Share Document