Effective feature selection technique in an integrated environment using enhanced principal component analysis

2020 ◽  
Author(s):  
D. Hemavathi ◽  
H. Srimathi
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Integrated Environment

scholarly journals A comparative study on dimensionality reduction between principal component analysis and k-means clustering

2019 ◽  
Vol 16 (2) ◽  
pp. 752
Author(s):  
Norsyela Muhammad Noor Mathivanan ◽  
Nor Azura Md.Ghani ◽  
Roziah Mohd Janor
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Time Complexity ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Model ◽  
Small Data ◽  
Feature Selection Technique ◽  
Data Set ◽  
Selection Technique

<span>The curse of dimensionality and the empty space phenomenon emerged as a critical problem in text classification. One way of dealing with this problem is applying a feature selection technique before performing a classification model. This technique helps to reduce the time complexity and sometimes increase the classification accuracy. This study introduces a feature selection technique using K-Means clustering to overcome the weaknesses of traditional feature selection technique such as principal component analysis (PCA) that require a lot of time to transform all the inputs data. This proposed technique decides on features to retain based on the significance value of each feature in a cluster. This study found that k-means clustering helps to increase the efficiency of KNN model for a large data set while KNN model without feature selection technique is suitable for a small data set. A comparison between K-Means clustering and PCA as a feature selection technique shows that proposed technique is better than PCA especially in term of computation time. Hence, k-means clustering is found to be helpful in reducing the data dimensionality with less time complexity compared to PCA without affecting the accuracy of KNN model for a high frequency data.</span>

scholarly journals Intrusion Detection System using One Class SVM with and without Feature Selection in Wormhole Attack Detection

2019 ◽  
Vol 9 (2S4) ◽  
pp. 629-638
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Ad Hoc ◽  
Detection System ◽  
Principal Component ◽  
Component Analysis ◽  
Svm Classifier ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Wormhole Attack

An Ad-hoc network is a kind of wireless construction from one to another computer, without having Wi-Fi access point or Router. However, the Ad hoc approach offers marginal security and decreases the data transfer rate. Consequently, it helps the attacker to connect with the ad-hoc network without any trouble. Therefore, a robust and reliable intrusion detection system (IDS) is a necessity of today’s information security domain. These IDS systems play a vital role in monitoring the threats encountered in a network by detecting the change in the normal profile due to attacks. Recently, to detect attacks the IDS are being equipped with machine learning algorithms to attain better accuracy and fast detection speed. Most of the IDS use different network features. However, enormous number of features makes the detection and prevention complicated. The IDS presented in this paper employs random forest and principal component analysis to minimize the number of features for network IDS for wireless ad hoc networks. The one class SVM has been used for detection of worm hole attack with and without feature selection. The performances of these approaches are compared with various existing techniques with false positive rate (FPR), accuracy and detection rate. Here, the accuracy improves and false positive rate reduces when intrusion is detected with feature selection technique. This paper discusses the performance of the one class SVM classifier in the wireless adhoc network IDS with random forest feature selection and principal component analysis feature selection techniques and one class SVM classifier without feature selection technique in the detection of wormhole attack. And the performance of one class SVM IDS is better in the detection of wormhole attack while it is implemented with principal component analysis feature selection technique.

Feature Selection for Classification using Principal Component Analysis and Information Gain

2021 ◽  
Vol 174 ◽  
pp. 114765 ◽  
Author(s):  
Erick Odhiambo Omuya ◽  
George Onyango Okeyo ◽  
Michael Waema Kimwele
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Information Gain ◽  
Principal Component ◽  
Component Analysis ◽  
Selection For

scholarly journals Neural network design and feature selection using principal component analysis and Taguchi method for identifying wood veneer defects

2014 ◽  
Vol 2 (1) ◽  
pp. 291-308 ◽  
Author(s):  
Baris Yuce ◽  
Ernesto Mastrocinque ◽  
Michael Sylvester Packianather ◽  
Duc Pham ◽  
Alfredo Lambiase ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Feature Selection ◽  
Taguchi Method ◽  
Network Design ◽  
Principal Component ◽  
Component Analysis ◽  
Wood Veneer
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