scholarly journals An Excellent Feature Selection Model Using Gradient-Based and Point Injection Techniques

2006 ◽  
pp. 679-692
Author(s):  
D. Huang ◽  
Tommy W. S. Chow
Keyword(s):  
Feature Selection ◽  
Selection Model ◽  
Point Injection ◽  
Gradient Based ◽  
Injection Techniques

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

scholarly journals Improved Feature Selection Model for Big Data Analytics

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 66989-67004 ◽  
Author(s):  
Ibrahim M. El-Hasnony ◽  
Sherif I. Barakat ◽  
Mohamed Elhoseny ◽  
Reham R. Mostafa
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Selection Model

scholarly journals CGA: a new feature selection model for visual human action recognition

2020 ◽  
Author(s):  
Ritam Guha ◽  
Ali Hussain Khan ◽  
Pawan Kumar Singh ◽  
Ram Sarkar ◽  
Debotosh Bhattacharjee
Keyword(s):  
Feature Selection ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Selection Model ◽  
New Feature

scholarly journals A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1046 ◽  
Author(s):  
Omar Almomani
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Selection Model ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Proposed Model ◽  
Positive Rate

The network intrusion detection system (NIDS) aims to identify virulent action in a network. It aims to do that through investigating the traffic network behavior. The approaches of data mining and machine learning (ML) are extensively used in the NIDS to discover anomalies. Regarding feature selection, it plays a significant role in improving the performance of NIDSs. That is because anomaly detection employs a great number of features that require much time. Therefore, the feature selection approach affects the time needed to investigate the traffic behavior and improve the accuracy level. The researcher of the present study aimed to propose a feature selection model for NIDSs. This model is based on the particle swarm optimization (PSO), grey wolf optimizer (GWO), firefly optimization (FFA) and genetic algorithm (GA). The proposed model aims at improving the performance of NIDSs. The proposed model deploys wrapper-based methods with the GA, PSO, GWO and FFA algorithms for selecting features using Anaconda Python Open Source, and deploys filtering-based methods for the mutual information (MI) of the GA, PSO, GWO and FFA algorithms that produced 13 sets of rules. The features derived from the proposed model are evaluated based on the support vector machine (SVM) and J48 ML classifiers and the UNSW-NB15 dataset. Based on the experiment, Rule 13 (R13) reduces the features into 30 features. Rule 12 (R12) reduces the features into 13 features. Rule 13 and Rule 12 offer the best results in terms of F-measure, accuracy and sensitivity. The genetic algorithm (GA) shows good results in terms of True Positive Rate (TPR) and False Negative Rate (FNR). As for Rules 11, 9 and 8, they show good results in terms of False Positive Rate (FPR), while PSO shows good results in terms of precision and True Negative Rate (TNR). It was found that the intrusion detection system with fewer features will increase accuracy. The proposed feature selection model for NIDS is rule-based pattern recognition to discover computer network attack which is in the scope of Symmetry journal.

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