Feature Selection Technique Impact for Internet Traffic Classification Using Naïve Bayesian

2015 ◽  
Vol 72 (5) ◽  
Author(s):  
Tony Antonio ◽  
Adi Suryaputra Paramita
Keyword(s):  
Feature Selection ◽  
Principal Component ◽  
Internet Traffic ◽  
The Internet ◽  
Traffic Classification ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Internet Traffic Classification

Feature selection technique has an important role for internet traffic classification. This technique will present more accurate data and more accurate internet traffic classification which will provide precise information for bandwidth optimization. One of the important considerations in the feature selection technique that should be looked into is how to choose the right features which can deliver better and more precise results for the classification process. This research will compare feature selection algorithms where the Internet traffic has the same correlation that could fit into the same class. Internet traffic dataset will be collected, formatted, classified and analyzed using Naïve Bayesian. Formerly, the Correlation Feature Selection (CFS) is used in the feature selection to find a collection of the best sub-sets data from the existing data but without the discriminant and principal of a body dataset. We plan to use Principal Component Analysis technique in order to find discriminant and principal feature for internet traffic classification. Moreover, this paper also studied the process to fit the features. The result also shows that the internet traffic classification using Naïve Bayesian and Correlation Feature Selection (CFS) have more than 90% accuracy while the classification accuracy reached 75% for feature selection using Principal Component Analysis (PCA).

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 A Novel Feature Selection Technique to Better Predict Climate Change Stage of Change

2021 ◽  
Vol 14 (1) ◽  
pp. 40
Author(s):  
Hamed Naseri ◽  
E. Owen D. Waygood ◽  
Bobin Wang ◽  
Zachary Patterson ◽  
Ricardo A. Daziano
Keyword(s):  
Climate Change ◽  
Feature Selection ◽  
Environmental Concern ◽  
Stage Of Change ◽  
Principal Component ◽  
Selection Methods ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Testing Data ◽  
New Feature

Indications of people’s environmental concern are linked to transport decisions and can provide great support for policymaking on climate change. This study aims to better predict individual climate change stage of change (CC-SoC) based on different features of transport-related behavior, General Ecological Behavior, New Environmental Paradigm, and socio-demographic characteristics. Together these sources result in over 100 possible features that indicate someone’s level of environmental concern. Such a large number of features may create several analytical problems, such as overfitting, accuracy reduction, and high computational costs. To this end, a new feature selection technique, named the Coyote Optimization Algorithm-Quadratic Discriminant Analysis (COA-QDA), is first proposed to find the optimal features to predict CC-SoC with the highest accuracy. Different conventional feature selection methods (Lasso, Elastic Net, Random Forest Feature Selection, Extra Trees, and Principal Component Analysis Feature Selection) are employed to compare with the COA-QDA. Afterward, eight classification techniques are applied to solve the prediction problem. Finally, a sensitivity analysis is performed to determine the most important features affecting the prediction of CC-SoC. The results indicate that COA-QDA outperforms conventional feature selection methods by increasing average testing data accuracy from 0.7 to 5.6%. Logistic Regression surpasses other classifiers with the highest prediction accuracy.

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