Interaction between feature subset selection techniques and machine learning classifiers for detecting unsolicited emails

2014 ◽  
Vol 14 (1) ◽  
pp. 53-61 ◽  
Author(s):  
Shrawan Kumar Trivedi ◽  
Shubhamoy Dey
Keyword(s):  
Machine Learning ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Defending Malicious Script Attacks Using Machine Learning Classifiers

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Nayeem Khan ◽  
Johari Abdullah ◽  
Adnan Shahid Khan
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Malicious Code ◽  
Supervised Machine Learning ◽  
Feature Subset ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Wrapper Method ◽  
Supervised Machine Learning Classifiers ◽  
Client Side

The web application has become a primary target for cyber criminals by injecting malware especially JavaScript to perform malicious activities for impersonation. Thus, it becomes an imperative to detect such malicious code in real time before any malicious activity is performed. This study proposes an efficient method of detecting previously unknown malicious java scripts using an interceptor at the client side by classifying the key features of the malicious code. Feature subset was obtained by using wrapper method for dimensionality reduction. Supervised machine learning classifiers were used on the dataset for achieving high accuracy. Experimental results show that our method can efficiently classify malicious code from benign code with promising results.

Intelligent Feature Subset Selection with Machine Learning based Risk Management for DAS Prediction

2021 ◽  
pp. 08-16
Author(s):  
Mohamed Abdel Abdel-Basset ◽  
◽  
◽  
Mohamed Elhoseny
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Feature Selection ◽  
Subset Selection ◽  
Classification Problem ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Time Machine ◽  
Primary Level ◽  
Stage Process

In the current epidemic situations, people are facing several mental disorders related to Depression, Anxiety, and Stress (DAS). Numerous scales are developed for computing the levels for DAS, and DAS-21 is one among them. At the same time, machine learning (ML) models are applied widely to resolve the classification problem efficiently, and feature selection (FS) approaches can be designed to improve the classifier results. In this aspect, this paper develops an intelligent feature selection with ML-based risk management (IFSML-RM) for DAS prediction. The IFSML-RM technique follows a two-stage process: quantum elephant herd optimization-based FS (QEHO-FS) and decision tree (DT) based classification. The QEHO algorithm utilizes the input data to select a valuable subset of features at the primary level. Then, the chosen features are fed into the DT classifier to determine the existence or non-existence of DAS. A detailed experimentation process is carried out on the benchmark dataset, and the experimental results showcased the betterment of the IFSML-RM technique in terms of different performance measures.

scholarly journals Improved Reasoning with Uncertainty Based Significant Feature Subset Selection for Alzheimer’s Disease Detection

2019 ◽  
Vol 8 (3) ◽  
pp. 3123-3131
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Significant Feature ◽  
Feature Subset ◽  
Shafer Theory

In this modern era neurodegenerative disorder of undefined causes affects the older adults and it becomes most cause of dementia. The Alzheimer’s disease is one of such neurodegenerative disorder which is very complex and hard to predict in the early stage. With evolving advancement in the field of machine learning, it is possible to predict the early stage of AD and diagnosing in initial stages may produce effect result for their further quality and healthy life. But uncertainty in determination of Alzheimer’s is a toughest challenge for the researchers in the field of machine learning. This paper aims to overcome the uncertainty in discovering dementia and non-dementia victims of Alzheimer’s by devising an improved reasoning with uncertainty based prominent feature subset selection using modified fuzzy dempster shafer theory (IRU-DST). For Alzheimer’s disease prediction the dataset is used form OASIS dataset. The performance of the proposed IRU-DST is validated using fuzzy artificial neural network. The simulation results proved the performance of the IRU-DST achieves better results comparing the other sate of arts, by gaining high accuracy rate and it also minimize the error rate considerably with the ability of handling uncertainty.

scholarly journals SVM and KNN Based SGO Feature Selection Algorithm for Breast Cancer Diagnosis

2020 ◽  
Vol 8 (2S7) ◽  
pp. 2237-2240
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Subset Selection ◽  
Machine Learning Algorithms ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm

In diagnosis and prediction systems, algorithms working on datasets with a high number of dimensions tend to take more time than those with fewer dimensions. Feature subset selection algorithms enhance the efficiency of Machine Learning algorithms in prediction problems by selecting a subset of the total features and thus pruning redundancy and noise. In this article, such a feature subset selection method is proposed and implemented to diagnose breast cancer using Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms. This feature selection algorithm is based on Social Group Optimization (SGO) an evolutionary algorithm. Higher accuracy in diagnosing breast cancer is achieved using our proposed model when compared to other feature selection-based Machine Learning algorithms

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