Feature selection using principal component analysis and genetic algorithm

Adhao, Rahul; Pachghare, Vinod

doi:10.1080/09720529.2020.1729507

Cited by 24 publications

(6 citation statements)

References 8 publications

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Section: Methodsmentioning

confidence: 99%

“…This method works by transforming the original variables of the data into a new set of uncorrelated variables, which means, high-dimensional data can be reduced to K dimensions by selecting the top K principal components, so as to achieving dimensionality reduction. 18,19 First, the data has to be preprocessed in order to remove noisy points. Then we have to calculate the covariance matrix of the preprocessed data.…”

Section: Flaws Characteristics Extraction Of Surface Defects and Data...mentioning

confidence: 99%

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Research on surface defect identification of steel balls based on improved K-CV parameter optimization support vector machine

Li,

Ren,

Feng

2023

Advances in Mechanical Engineering

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Surface defects generated during the production process of steel balls can lead to bearing failures, which makes it crucial to promptly detect and classify these defects. Defects classify is helpful for analysis and improving the production process. An algorithm that incorporates K-fold cross-validation (K-CV) with improved grid search is proposed to optimize the parameters of SVM, in order to detect surface defects with steel balls. Principal Component Analysis (PCA) was employed to reduce the dimensionality of the effective features data. The K-CV algorithm was employed in conjunction with an improved grid search method to find the optimal parameters “c” and “g.” This approach not only reduced the search time but also diminished the influence of individual samples on the model, thereby enhancing its robustness and ultimately improving the classification accuracy. The model’s performance was evaluated using a confusion matrix, and a comparison was made with three other machine learning models. The experimental results demonstrated the effectiveness of the proposed algorithm in classifying defects on highly reflective metal surfaces such as steel balls. The model achieved a classification accuracy of 97.15%.

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Section: Methodsmentioning

confidence: 99%

Section: Flaws Characteristics Extraction Of Surface Defects and Data...mentioning

confidence: 99%

Research on surface defect identification of steel balls based on improved K-CV parameter optimization support vector machine

Li,

Ren,

Feng

2023

Advances in Mechanical Engineering

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“…Experimental results conclude that the j48 with MI features selection method achieved the highest accuracy in web-attack detection. Another study [26] uses Genetic Algorithm (GA) and Principal Component Analysis (PCA), for feature selection from intrusion datasets. Attacks are detected using a Decision Tree (DT) classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine-Learning-Based DDoS Attack Detection Using Mutual Information and Random Forest Feature Importance Method

et al. 2022

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Cloud computing facilitates the users with on-demand services over the Internet. The services are accessible from anywhere at any time. Despite the valuable services, the paradigm is, also, prone to security issues. A Distributed Denial of Service (DDoS) attack affects the availability of cloud services and causes security threats to cloud computing. Detection of DDoS attacks is necessary for the availability of services for legitimate users. The topic has been studied by many researchers, with better accuracy for different datasets. This article presents a method for DDoS attack detection in cloud computing. The primary objective of this article is to reduce misclassification error in DDoS detection. In the proposed work, we select the most relevant features, by applying two feature selection techniques, i.e., the Mutual Information (MI) and Random Forest Feature Importance (RFFI) methods. Random Forest (RF), Gradient Boosting (GB), Weighted Voting Ensemble (WVE), K Nearest Neighbor (KNN), and Logistic Regression (LR) are applied to selected features. The experimental results show that the accuracy of RF, GB, WVE, and KNN with 19 features is 0.99. To further study these methods, misclassifications of the methods are analyzed, which lead to more accurate measurements. Extensive experiments conclude that the RF performed well in DDoS attack detection and misclassified only one attack as normal. Comparative results are presented to validate the proposed method.

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“…Although PCA and GA have shown significant optimization of ML algorithms using an intelligent search-based mechanism [151,152], further improvement is necessary to achieve better results. Even in antenna arrays, the performance of ML techniques is not satisfactory due to the increased possibilities of antenna combinations.…”

Section: Limitationsmentioning

confidence: 99%

Application of Machine Learning in Electromagnetics: Mini-Review

et al. 2021

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As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.

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Feature selection using principal component analysis and genetic algorithm

Cited by 24 publications

References 8 publications

Research on surface defect identification of steel balls based on improved K-CV parameter optimization support vector machine

Research on surface defect identification of steel balls based on improved K-CV parameter optimization support vector machine

Machine-Learning-Based DDoS Attack Detection Using Mutual Information and Random Forest Feature Importance Method

Application of Machine Learning in Electromagnetics: Mini-Review

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