Comparison of Support Vector Machine Recursive Feature Elimination and Kernel Function as feature selection using Support Vector Machine for lung cancer classification

Rustam, Zuherman; Kharis, Selly Anastassia Amellia

doi:10.1088/1742-6596/1442/1/012027

Cited by 19 publications

(9 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…SVM is one of the basic approaches for supervised learning. Additionally, it is widely used in classification and regression applications, and also frequently in clustering [ 43 , 44 ], feature selection [ 45 – 47 ], feature extraction [ 48 , 49 ], etc. SVM, based on the statistical learning theory [ 50 , 51 ], is a distribution independent learning algorithm since it does not require joint distribution function information.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms

Canayaz

Şehrïbanoğlu

Ozdag

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

Early diagnosis of COVID-19, the new coronavirus disease, is considered important for the treatment and control of this disease. The diagnosis of COVID-19 is based on two basic approaches of laboratory and chest radiography, and there has been a significant increase in studies performed in recent months by using chest computed tomography (CT) scans and artificial intelligence techniques. Classification of patient CT scans results in a serious loss of radiology professionals' valuable time. Considering the rapid increase in COVID-19 infections, in order to automate the analysis of CT scans and minimize this loss of time, in this paper a new method is proposed using BO (BO)-based MobilNetv2, ResNet-50 models, SVM and kNN machine learning algorithms. In this method, an accuracy of 99.37% was achieved with an average precision of 99.38%, 99.36% recall and 99.37% F-score on datasets containing COVID and non-COVID classes. When we examine the performance results of the proposed method, it is predicted that it can be used as a decision support mechanism with high classification success for the diagnosis of COVID-19 with CT scans.

show abstract

Section: Methodsmentioning

confidence: 99%

COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms

Canayaz

Şehrïbanoğlu

Ozdag

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Here we take the advantage of SVM as a powerful supervised machine learning technique suited to our experimental work, as it is based on statistical learning theory and can be effectively trained to classify face biometric data by determining an optimal set of support vectors, which are members of the labeled face training data samples and nominated to form a discriminative SVM classifier. The main objective of an SVM is to find optimal hyperplanes to separate data with a maximum margin [34]. SVM can separate linear and non-linear data by applying different kernel functions such as linear function, Radial Basis Function (RBF), polynomial function, etc.…”

Section: Training Svm Classifiermentioning

confidence: 99%

Dynamic Feature Subset Selection for Occluded Face Recognition

Alsaedi¹,

Jaha²

2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Accurate recognition of person identity is a critical task in civil society for various application and different needs. There are different well-established biometric modalities that can be used for recognition purposes such as face, voice, fingerprint, iris, etc. Recently, face images have been widely used for person recognition, since the human face is the most natural and user-friendly recognition method. However, in real-life applications, some factors may degrade the recognition performance, such as partial face occlusion, poses, illumination conditions, facial expressions, etc. In this paper, we propose two dynamic feature subset selection (DFSS) methods to achieve better recognition for occluded faces. The proposed DFSS methods are based on resilient algorithms attempting to minimize the negative influence of confusing and low-quality features extracted from occluded areas by either exclusion or weight reduction. Principal Component Analysis and Gabor filtering based approaches are initially used for face feature extraction, then the proposed DFSS methods are dynamically enforced. This is leading to more effective feature representation and an improved recognition performance. To validate their effectiveness, multiple experiments are conducted and the performance of different methods is compared. The experimental work is carried out using AR database and Extended Yale Face Database B. The obtained results of face identification and verification show that both proposed DFSS methods outperform the standard (static) use of the whole number of features and the equal feature weights.

show abstract

“…To investigate the validity and potency of the dynamic fusion framework for improving the recognition performance, we train and use an SVM-based classifier for experimenting the proposed dynamic fusion approaches. The idea of SVM is based on structural risk minimization that tries to find an optimal hyperplane that maximizes the margin between classes [59,60]. The separation can be tuned by the C value (known as regularization parameter or penalty factor), which refers to the softness of the margin.…”

Section: Classifier Trainingmentioning

confidence: 99%

Dynamic Audio-Visual Biometric Fusion for Person Recognition

Ghalandari¹,

Mahariq²,

Ghadak³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Biometric recognition refers to the process of recognizing a person's identity using physiological or behavioral modalities, such as face, voice, fingerprint, gait, etc. Such biometric modalities are mostly used in recognition tasks separately as in unimodal systems, or jointly with two or more as in multimodal systems. However, multimodal systems can usually enhance the recognition performance over unimodal systems by integrating the biometric data of multiple modalities at different fusion levels. Despite this enhancement, in real-life applications some factors degrade multimodal systems' performance, such as occlusion, face poses, and noise in voice data. In this paper, we propose two algorithms that effectively apply dynamic fusion at feature level based on the data quality of multimodal biometrics. The proposed algorithms attempt to minimize the negative influence of confusing and low-quality features by either exclusion or weight reduction to achieve better recognition performance. The proposed dynamic fusion was achieved using face and voice biometrics, where face features were extracted using principal component analysis (PCA), and Gabor filters separately, whilst voice features were extracted using Mel-Frequency Cepstral Coefficients (MFCCs). Here, the facial data quality assessment of face images is mainly based on the existence of occlusion, whereas the assessment of voice data quality is substantially based on the calculation of signal to noise ratio (SNR) as per the existence of noise. To evaluate the performance of the proposed algorithms, several experiments were conducted using two combinations of three different databases, AR database, and the extended Yale Face Database B for face images, in addition to VOiCES database for voice data. The obtained results show that both proposed dynamic fusion algorithms attain improved performance and offer more advantages in identification and verification over not only the standard unimodal algorithms but also the multimodal algorithms using standard fusion methods.

show abstract

Comparison of Support Vector Machine Recursive Feature Elimination and Kernel Function as feature selection using Support Vector Machine for lung cancer classification

Cited by 19 publications

References 11 publications

COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms

COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms

Dynamic Feature Subset Selection for Occluded Face Recognition

Dynamic Audio-Visual Biometric Fusion for Person Recognition

Contact Info

Product

Resources

About