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 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

scholarly journals Feature Subset Selection for Malware Detection in Smart IoT Platforms

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1374
Author(s):  
Jemal Abawajy ◽  
Abdulbasit Darem ◽  
Asma A. Alhashmi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Predictive Accuracy ◽  
Malware Detection ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Selection Methods ◽  
Android Malware ◽  
Selection Algorithms

Malicious software (“malware”) has become one of the serious cybersecurity issues in Android ecosystem. Given the fast evolution of Android malware releases, it is practically not feasible to manually detect malware apps in the Android ecosystem. As a result, machine learning has become a fledgling approach for malware detection. Since machine learning performance is largely influenced by the availability of high quality and relevant features, feature selection approaches play key role in machine learning based detection of malware. In this paper, we formulate the feature selection problem as a quadratic programming problem and analyse how commonly used filter-based feature selection methods work with emphases on Android malware detection. We compare and contrast several feature selection methods along several factors including the composition of relevant features selected. We empirically evaluate the predictive accuracy of the feature subset selection algorithms and compare their predictive accuracy and the execution time using several learning algorithms. The results of the experiments confirm that feature selection is necessary for improving accuracy of the learning models as well decreasing the run time. The results also show that the performance of the feature selection algorithms vary from one learning algorithm to another and no one feature selection approach performs better than the other approaches all the time.

scholarly journals Symmetry based Feature Selection with Multi layer Perceptron for the prediction of Chronic Disease

2019 ◽  
Vol 8 (2) ◽  
pp. 3316-3322
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Feature Selection ◽  
Chronic Disease ◽  
Multilayer Perceptron ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Selection Technique ◽  
Health Care Analytics

Huge amount of Healthcare data are produced every day from the various health care sectors. The accumulated data can be effectively analyzed to identify people's risk from chronic diseases. The process of predicting the presence or absence of the disease and also to diagnosing the various disease using the historical medical data is known as Health Care Analytics. Health care analytics will improve patient care and also the harness practice of medical practitioner. The feature selection is considered as a core aspect of the machine learning which hugely contribute towards the performance of the machine learning model. In this paper symmetry based feature subset selection is proposed to select the optimal features from the Health care data which contribute towards the prediction outcome. The Multilayer perceptron algorithm(MLP) used as a classifier which will predict the outcome by using the features which are selected from the Symmetry-based feature subset selection technique. The chronic disease dataset Diabetes, Cancer, Breast Cancer, and Heart Disease data set accumulated from UCI repository is used to conduct the experiment. The experimental results demonstrate that the proposed hybrid combination of feature selection technique and the multilayer perceptron outperforms in accuracy compare to the existing approaches.

scholarly journals Angle Modulated Artificial Bee Colony Algorithms for Feature Selection

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Gürcan Yavuz ◽  
Doğan Aydin
Keyword(s):  
Feature Selection ◽  
Artificial Bee Colony ◽  
Continuous Optimization ◽  
Subset Selection ◽  
Machine Intelligence ◽  
Feature Subset Selection ◽  
High Dimensional ◽  
Feature Subset ◽  
Bee Colony ◽  
Angle Modulation

Optimal feature subset selection is an important and a difficult task for pattern classification, data mining, and machine intelligence applications. The objective of the feature subset selection is to eliminate the irrelevant and noisy feature in order to select optimum feature subsets and increase accuracy. The large number of features in a dataset increases the computational complexity thus leading to performance degradation. In this paper, to overcome this problem, angle modulation technique is used to reduce feature subset selection problem to four-dimensional continuous optimization problem instead of presenting the problem as a high-dimensional bit vector. To present the effectiveness of the problem presentation with angle modulation and to determine the efficiency of the proposed method, six variants of Artificial Bee Colony (ABC) algorithms employ angle modulation for feature selection. Experimental results on six high-dimensional datasets show that Angle Modulated ABC algorithms improved the classification accuracy with fewer feature subsets.

scholarly journals A Hybrid Feature Selection Method for Improve the Accuracy of Medical Classification Process

2021 ◽  
Vol 11 (1) ◽  
pp. 50-55
Author(s):  
Maria Mohammad Yousef ◽  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Fitness Function ◽  
Machine Learning Algorithms ◽  
Feature Subset Selection ◽  
High Dimensionality ◽  
Support Vector ◽  
Feature Subset

Generally, medical dataset classification has become one of the biggest problems in data mining research. Every database has a given number of features but it is observed that some of these features can be redundant and can be harmful as well as disrupt the process of classification and this problem is known as a high dimensionality problem. Dimensionality reduction in data preprocessing is critical for increasing the performance of machine learning algorithms. Besides the contribution of feature subset selection in dimensionality reduction gives a significant improvement in classification accuracy. In this paper, we proposed a new hybrid feature selection approach based on (GA assisted by KNN) to deal with issues of high dimensionality in biomedical data classification. The proposed method first applies the combination between GA and KNN for feature selection to find the optimal subset of features where the classification accuracy of the k-Nearest Neighbor (kNN) method is used as the fitness function for GA. After selecting the best-suggested subset of features, Support Vector Machine (SVM) are used as the classifiers. The proposed method experiments on five medical datasets of the UCI Machine Learning Repository. It is noted that the suggested technique performs admirably on these databases, achieving higher classification accuracy while using fewer features.

scholarly journals Accelerated Simulated Annealing and Mutation Operator Feature Selection method for Big Data

2019 ◽  
Vol 8 (2) ◽  
pp. 910-916
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Feature Selection Method ◽  
Classification Problem ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Mutation Operator ◽  
Knn Classifier ◽  
Optimal Feature Subset ◽  
Optimal Feature

The optimal feature subset selection over very high dimensional data is a vital issue. Even though the optimal features are selected, the classification of those selected features becomes a key complicated task. In order to handle these problems, a novel, Accelerated Simulated Annealing and Mutation Operator (ASAMO) feature selection algorithm is suggested in this work. For solving the classification problem, the Fuzzy Minimal Consistent Class Subset Coverage (FMCCSC) problem is introduced. In FMCCSC, consistent subset is combined with the K-Nearest Neighbour (KNN) classifier known as FMCCSC-KNN classifier. The two data sets Dorothea and Madelon from UCI machine repository are experimented for optimal feature selection and classification. The experimental results substantiate the efficiency of proposed ASAMO with FMCCSC-KNN classifier compared to Particle Swarm Optimization (PSO) and Accelerated PSO feature selection algorithms.

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