scholarly journals A Grey Wolf Optimizer Feature Selection method and its Effect on the Performance of Document Classification Problem

2021 ◽  
Vol 4 (2) ◽  
pp. 116-122
Author(s):  
Ibraheem Al-Jadir ◽  
Waleed A. Mahmoud
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Optimization Methods ◽  
Classification Problem ◽  
Performance Outcomes ◽  
Grey Wolf Optimizer ◽  
Great Success ◽  
Grey Wolf ◽  
Feature Selection Problem ◽  
Krill Herd

Optimization methods are considered as one of the highly developed areas in Artificial Intelligence (AI). The success of the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) has encouraged researchers to develop other methods that can obtain better performance outcomes and to be more responding to the modern needs. The Grey Wolf Optimization (GWO), and the Krill Herd (KH) are some of those methods that showed a great success in different applications in the last few years. In this paper, we propose a comparative study of using different optimization methods including KH and GWO in order to solve the problem of document feature selection for the classification problem. These methods are used to model the feature selection problem as a typical optimization method. Due to the complexity and the non-linearity of this kind of problems, it becomes necessary to use some advanced techniques to make the judgement of which features subset that is optimal to enhance the performance of classification of text documents. The test results showed the superiority of GWO over the other counterparts using the specified evaluation measures.

scholarly journals A New Feature Selection Method Based on a Self-Variant Genetic Algorithm Applied to Android Malware Detection

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1290
Author(s):  
Le Wang ◽  
Yuelin Gao ◽  
Shanshan Gao ◽  
Xin Yong
Keyword(s):  
Feature Selection ◽  
Population Size ◽  
Feature Selection Method ◽  
Classification Problem ◽  
Selection Method ◽  
Classification Problems ◽  
Feature Selection Problem ◽  
Android Malware ◽  
Android Malware Detection ◽  
Mutation Operators

In solving classification problems in the field of machine learning and pattern recognition, the pre-processing of data is particularly important. The processing of high-dimensional feature datasets increases the time and space complexity of computer processing and reduces the accuracy of classification models. Hence, the proposal of a good feature selection method is essential. This paper presents a new algorithm for solving feature selection, retaining the selection and mutation operators from traditional genetic algorithms. On the one hand, the global search capability of the algorithm is ensured by changing the population size, on the other hand, finding the optimal mutation probability for solving the feature selection problem based on different population sizes. During the iteration of the algorithm, the population size does not change, no matter how many transformations are made, and is the same as the initialized population size; this spatial invariance is physically defined as symmetry. The proposed method is compared with other algorithms and validated on different datasets. The experimental results show good performance of the algorithm, in addition to which we apply the algorithm to a practical Android software classification problem and the results also show the superiority of the algorithm.

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 A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification

Computers ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 58 ◽  
Author(s):  
Jingwei Too ◽  
Abdul Abdullah ◽  
Norhashimah Mohd Saad ◽  
Nursabillilah Mohd Ali ◽  
Weihown Tee
Keyword(s):  
Feature Selection ◽  
Feature Reduction ◽  
Selection Problem ◽  
Grey Wolf Optimizer ◽  
Binary Particle Swarm Optimization ◽  
Feature Subset ◽  
Grey Wolf ◽  
Feature Selection Problem ◽  
Time Frequency ◽  
Emg Signal

Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this article proposes a new competitive binary grey wolf optimizer (CBGWO) to solve the feature selection problem in EMG signals classification. Initially, short-time Fourier transform (STFT) transforms the EMG signal into time-frequency representation. Ten time-frequency features are extracted from the STFT coefficient. Then, the proposed method is used to evaluate the optimal feature subset from the original feature set. To evaluate the effectiveness of proposed method, CBGWO is compared with binary grey wolf optimization (BGWO1 and BGWO2), binary particle swarm optimization (BPSO), and genetic algorithm (GA). The experimental results show the superiority of CBGWO not only in classification performance, but also feature reduction. In addition, CBGWO has a very low computational cost, which is more suitable for real world application.

scholarly journals Onboard Cloud-enabled Parkinson Disease Identification System Using Enhanced Grey Wolf Optimization

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1360-1372
Author(s):  
Ramaprabha Jayaram ◽  
T. Senthil Kumar
Keyword(s):  
Feature Selection ◽  
Parkinson Disease ◽  
Neurodegenerative Disorder ◽  
Feature Selection Method ◽  
Selection Method ◽  
Identification System ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Time Duration ◽  
Disease Identification

Parkinson disease is a rigorous neurodegenerative disorder characterized by the cognitive behavior ending with disability problems. Especially, the elderly people should be given more care and spend more time duration to diagnose when they are at risk. It is more important to identify and diagnose Parkinson disease at an earlier stage rather than spending too much of cost later stages. Different ways of diagnosing the disease ranging from gene analysis to gait behavior, speech, writing test and olfactory models were used in the conventional testing process. In order to increase the patient’s quality of life and minimize the cost of healthcare utilization, an Onboard Cloud-Enabled Parkinson Disease Identification System (OCPDIS) is proposed. An enhanced grey wolf optimization is explored along with the differential evolution techniques to form an effective hybrid feature selection method. Using this feature selection method in the enhanced k-Nearest Neighbor (k-NN) classifier model could substantially improve the prediction time and prediction accuracy.

DeepCPP: a deep neural network based on nucleotide bias information and minimum distribution similarity feature selection for RNA coding potential prediction

2020 ◽  
Author(s):  
Yu Zhang ◽  
Cangzhi Jia ◽  
Melissa Jane Fullwood ◽  
Chee Keong Kwoh
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Deep Neural Network ◽  
Feature Selection Method ◽  
Classification Problem ◽  
Open Reading Frames ◽  
Nucleotide Bias ◽  
Sequencing Technologies ◽  
Type Data ◽  
Coding Potential

Abstract The development of deep sequencing technologies has led to the discovery of novel transcripts. Many in silico methods have been developed to assess the coding potential of these transcripts to further investigate their functions. Existing methods perform well on distinguishing majority long noncoding RNAs (lncRNAs) and coding RNAs (mRNAs) but poorly on RNAs with small open reading frames (sORFs). Here, we present DeepCPP (deep neural network for coding potential prediction), a deep learning method for RNA coding potential prediction. Extensive evaluations on four previous datasets and six new datasets constructed in different species show that DeepCPP outperforms other state-of-the-art methods, especially on sORF type data, which overcomes the bottleneck of sORF mRNA identification by improving more than 4.31, 37.24 and 5.89% on its accuracy for newly discovered human, vertebrate and insect data, respectively. Additionally, we also revealed that discontinuous k-mer, and our newly proposed nucleotide bias and minimal distribution similarity feature selection method play crucial roles in this classification problem. Taken together, DeepCPP is an effective method for RNA coding potential prediction.

A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection

2020 ◽  
Vol 139 ◽  
pp. 112824 ◽  
Author(s):  
Mohamed Abdel-Basset ◽  
Doaa El-Shahat ◽  
Ibrahim El-henawy ◽  
Victor Hugo C. de Albuquerque ◽  
Seyedali Mirjalili
Keyword(s):  
Feature Selection ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Two Phase ◽  
Phase Mutation
Sign in / Sign up

Export Citation Format

Share Document