scholarly journals Effective Feature Selection for 5G IM Applications Traffic Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Muhammad Shafiq ◽  
Xiangzhan Yu ◽  
Asif Ali Laghari ◽  
Dawei Wang
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Statistical Test ◽  
Traffic Classification ◽  
Features Selection ◽  
Traffic Flows ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Wrapper Method ◽  
Selection For

Recently, machine learning (ML) algorithms have widely been applied in Internet traffic classification. However, due to the inappropriate features selection, ML-based classifiers are prone to misclassify Internet flows as that traffic occupies majority of traffic flows. To address this problem, a novel feature selection metric named weighted mutual information (WMI) is proposed. We develop a hybrid feature selection algorithm named WMI_ACC, which filters most of the features with WMI metric. It further uses a wrapper method to select features for ML classifiers with accuracy (ACC) metric. We evaluate our approach using five ML classifiers on the two different network environment traces captured. Furthermore, we also apply Wilcoxon pairwise statistical test on the results of our proposed algorithm to find out the robust features from the selected set of features. Experimental results show that our algorithm gives promising results in terms of classification accuracy, recall, and precision. Our proposed algorithm can achieve 99% flow accuracy results, which is very promising.

scholarly journals High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1238
Author(s):  
Supanat Chamchuen ◽  
Apirat Siritaratiwat ◽  
Pradit Fuangfoo ◽  
Puripong Suthisopapan ◽  
Pirat Khunkitti
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Distribution System ◽  
Classification Accuracy ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Electrical Distribution ◽  
Power Quality Disturbance ◽  
Optimal Feature Selection ◽  
Optimal Feature

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

A hybrid feature selection algorithm combining ReliefF and Particle swarm optimization for high-dimensional medical data

2021 ◽  
pp. 1-15
Author(s):  
Zhaozhao Xu ◽  
Derong Shen ◽  
Yue Kou ◽  
Tiezheng Nie
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Random Forest ◽  
Classification Accuracy ◽  
Particle Swarm ◽  
Medical Data ◽  
High Dimensional ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Swarm Optimization

Due to high-dimensional feature and strong correlation of features, the classification accuracy of medical data is not as good enough as expected. feature selection is a common algorithm to solve this problem, and selects effective features by reducing the dimensionality of high-dimensional data. However, traditional feature selection algorithms have the blindness of threshold setting and the search algorithms are liable to fall into a local optimal solution. Based on it, this paper proposes a hybrid feature selection algorithm combining ReliefF and Particle swarm optimization. The algorithm is mainly divided into three parts: Firstly, the ReliefF is used to calculate the feature weight, and the features are ranked by the weight. Then ranking feature is grouped according to the density equalization, where the density of features in each group is the same. Finally, the Particle Swarm Optimization algorithm is used to search the ranking feature groups, and the feature selection is performed according to a new fitness function. Experimental results show that the random forest has the highest classification accuracy on the features selected. More importantly, it has the least number of features. In addition, experimental results on 2 medical datasets show that the average accuracy of random forest reaches 90.20%, which proves that the hybrid algorithm has a certain application value.

Byte2vec: Malware Representation and Feature Selection for Android

2019 ◽  
Vol 63 (8) ◽  
pp. 1125-1138
Author(s):  
Mahmood Yousefi-Azar ◽  
Len Hamey ◽  
Vijay Varadharajan ◽  
Shiping Chen
Keyword(s):  
Feature Selection ◽  
Feature Vector ◽  
False Negative ◽  
Malware Detection ◽  
False Negative Rate ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Context Vector ◽  
Wide Range ◽  
Selection For

Abstract Malware detection based on static features and without code disassembling is a challenging path of research. Obfuscation makes the static analysis of malware even more challenging. This paper extends static malware detection beyond byte level $n$-grams and detecting important strings. We propose a model (Byte2vec) with the capabilities of both binary file feature representation and feature selection for malware detection. Byte2vec embeds the semantic similarity of byte level codes into a feature vector (byte vector) and also into a context vector. The learned feature vectors of Byte2vec, using skip-gram with negative-sampling topology, are combined with byte-level term-frequency (tf) for malware detection. We also show that the distance between a feature vector and its corresponding context vector provides a useful measure to rank features. The top ranked features are successfully used for malware detection. We show that this feature selection algorithm is an unsupervised version of mutual information (MI). We test the proposed scheme on four freely available Android malware datasets including one obfuscated malware dataset. The model is trained only on clean APKs. The results show that the model outperforms MI in a low-dimensional feature space and is competitive with MI and other state-of-the-art models in higher dimensions. In particular, our tests show very promising results on a wide range of obfuscated malware with a false negative rate of only 0.3% and a false positive rate of 2.0%. The detection results on obfuscated malware show the advantage of the unsupervised feature selection algorithm compared with the MI-based method.

scholarly journals Improved Measures of Redundancy and Relevance for mRMR Feature Selection

Computers ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 42 ◽  
Author(s):  
Insik Jo ◽  
Sangbum Lee ◽  
Sejong Oh
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Computing Time ◽  
Performance Comparison ◽  
Medical Data ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Learning Tasks ◽  
R Value ◽  
Measure Of Performance

Many biological or medical data have numerous features. Feature selection is one of the data preprocessing steps that can remove the noise from data as well as save the computing time when the dataset has several hundred thousand or more features. Another goal of feature selection is improving the classification accuracy in machine learning tasks. Minimum Redundancy Maximum Relevance (mRMR) is a well-known feature selection algorithm that selects features by calculating redundancy between features and relevance between features and class vector. mRMR adopts mutual information theory to measure redundancy and relevance. In this research, we propose a method to improve the performance of mRMR feature selection. We apply Pearson’s correlation coefficient as a measure of redundancy and R-value as a measure of relevance. To compare original mRMR and the proposed method, features were selected using both of two methods from various datasets, and then we performed a classification test. The classification accuracy was used as a measure of performance comparison. In many cases, the proposed method showed higher accuracy than original mRMR.

scholarly journals A Systematic Algorithm for Data Cluster Using Map-Reduce Approach

2019 ◽  
pp. 564-569
Author(s):  
Kechika. S ◽  
Sapthika. B ◽  
Keerthana. B ◽  
Abinaya. S ◽  
Abdulfaiz. A
Keyword(s):  
Feature Selection ◽  
Map Reduce ◽  
Features Selection ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Main Concept ◽  
Selection Approach ◽  
Clustering Data ◽  
Data Objects ◽  
Time Required

We have been studying the problem clustering data objects as we have implemented a new algorithm called algorithm of clustering data using map reduce approach. In cluster, main part is feature selection which involves in recognition of set of features of a subset, since feature selection is considered as a important process. They also produces the approximate and according requests with the original set of features used in this type of approach. The main concept beyond this paper is to give the outcome of the clustering features. This paper which also gives the knowledge about cluster and it's own process. To processing of large datasets the nature of clustering where some more concepts are more helpful and important in a clustering process. In a clustering methodology where more concepts are very useful. The feature selection algorithm which affects, the entire process of clustering is the map-reduce concept. since, feature selection or extraction which is also used in map-reduce approach. The most desirable component is time complexity where efficiency concerns in this criterion. Here time required to find the effective features, where features of quality subsets is equal to effectiveness. The complexity to find based on this criteria based map-reduce features selection approach, which is proposed and evaluated in this paper.

An Improved Feature Selection Algorithm Based on Parzen Window and Conditional Mutual Information

2013 ◽  
Vol 347-350 ◽  
pp. 2614-2619
Author(s):  
Deng Chao He ◽  
Wen Ning Hao ◽  
Gang Chen ◽  
Da Wei Jin
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Probability Density Functions ◽  
Density Functions ◽  
Continuous Variables ◽  
Conditional Mutual Information ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Parzen Window ◽  
Selection For

In this paper, an improved feature selection algorithm by conditional mutual information with Parzen window was proposed, which adopted conditional mutual information as an evaluation criterion of feature selection in order to overcome the deficiency of feature redundant and used Parzen window to estimate the probability density functions and calculate the conditional mutual information of continuous variables, in such a way as to achieve feature selection for continuous data.

scholarly journals Improving K-NN Internet Traffic Classification Using Clustering and Principle Component Analysis

2017 ◽  
Vol 6 (2) ◽  
pp. 159-165
Author(s):  
Adi Suryaputra Paramita
Keyword(s):  
Feature Selection ◽  
Component Analysis ◽  
Internet Traffic ◽  
Classification Algorithm ◽  
Combination Method ◽  
Traffic Classification ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Initial Dataset ◽  
Internet Traffic Classification

K-Nearest Neighbour (K-NN) is one of the popular classification algorithm, in this research K-NN use to classify internet traffic, the K-NN is appropriate for huge amounts of data and have more accurate classification, K-NN algorithm has a disadvantages in computation process because K-NN algorithm calculate the distance of all existing data in dataset. Clustering is one of the solution to conquer the K-NN weaknesses, clustering process should be done before the K-NN classification process, the clustering process does not need high computing time to conqest the data which have same characteristic, Fuzzy C-Mean is the clustering algorithm used in this research. The Fuzzy C-Mean algorithm no need to determine the first number of clusters to be formed, clusters that form on this algorithm will be formed naturally based datasets be entered. The Fuzzy C-Mean has weakness in clustering results obtained are frequently not same even though the input of dataset was same because the initial dataset that of the Fuzzy C-Mean is less optimal, to optimize the initial datasets needs feature selection algorithm. Feature selection is a method to produce an optimum initial dataset Fuzzy C-Means. Feature selection algorithm in this research is Principal Component Analysis (PCA). PCA can reduce non significant attribute or feature to create optimal dataset and can improve performance for clustering and classification algorithm. The resultsof this research is the combination method of classification, clustering and feature selection of internet traffic dataset was successfully modeled internet traffic classification method that higher accuracy and faster performance.

scholarly journals Naive Bayes-Guided Bat Algorithm for Feature Selection

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Ahmed Majid Taha ◽  
Aida Mustapha ◽  
Soong-Der Chen
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Naive Bayes ◽  
Bat Algorithm ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Benchmark Datasets ◽  
Selection Algorithms

When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets.

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