A hybrid ensemble-filter wrapper feature selection approach for medical data classification

Singh, Namrata; Singh, Pradeep

doi:10.1016/j.chemolab.2021.104396

Cited by 71 publications

(24 citation statements)

References 71 publications

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“…Classifiers like Decision Tree (DT), K-Nearest Neighbor (KNN), and SVM are used for the performance evaluation of selected optimal genes. A hybrid ensemble-filter wrapper feature selection approach is described in [15] for medical data classification. In this model, authors have adopted a subset-based filter approach such as Correlation-based Feature Selection (CFS) and Consistency (CONS) and a rank-based filter approach such as chi-square test, information gain, and relieff for initial gene selection.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Hybrid Ensemble Gene Selection Model for Autism Using DNN

Anurekha¹,

Geetha²

2023

Intelligent Automation &Amp; Soft Computing

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Autism Spectrum Disorder (ASD) is a complicated neurodevelopmental disorder that is often identified in toddlers. The microarray data is used as a diagnostic tool to identify the genetics of the disorder. However, microarray data is large and has a high volume. Consequently, it suffers from the problem of dimensionality. In microarray data, the sample size and variance of the gene expression will lead to overfitting and misclassification. Identifying the autism gene (feature) subset from microarray data is an important and challenging research area. It has to be efficiently addressed to improve gene feature selection and classification. To overcome the challenges, a novel Intelligent Hybrid Ensemble Gene Selection (IHEGS) model is proposed in this paper. The proposed model integrates the intelligence of different feature selection techniques over the data partitions. In this model, the initial gene selection is carried out by data perturbation, and the final autism gene subset is obtained by functional perturbation, which reduces the problem of dimensionality in microarray data. The functional perturbation module employs three meta-heuristic swarm intelligence-based techniques for gene selection. The obtained gene subset is validated by the Deep Neural Network (DNN) model. The proposed model is implemented using python with six National Center for Biotechnology Information (NCBI) gene expression datasets. From the comparative study with other existing state-of-the-art systems, the proposed model provides stable results in terms of feature selection and classification accuracy.

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Section: Related Workmentioning

confidence: 99%

An Intelligent Hybrid Ensemble Gene Selection Model for Autism Using DNN

Anurekha¹,

Geetha²

2023

Intelligent Automation &Amp; Soft Computing

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“…It is mainly utilized on higher dimension information to estimate the efficacy of attributes in classification. IGFR measures the worth of attributes by estimating the IGFR of features regarding the targeted class (Singh and Singh 2021). Indeed, IGFR calculates the number of data needed for predicting the targeted class by knowing the absence or presence of an attribute.…”

Section: Feature Reductionmentioning

confidence: 99%

“…ðReject whereby marginal entropy is embodied as HðÞ and conditional entropy of y shown a is provided as HðcjaÞ: The IGFR is a fast filter-based FS approach whereby the attribute is graded in descending order of IG score and is carefully chosen according to threshold. High IG implies better discriminatory power for making decisions (Singh and Singh 2021).…”

Section: Feature Reductionmentioning

confidence: 99%

Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crops Type Classification Using Hyperspectral Images

Escorcia‐Gutierrez¹,

Gamarra

Torres-Torres

et al. 2022

Canadian Journal of Remote Sensing

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Hyperspectral Remote Sensing (HRS) is an emergent, multidisciplinary paradigm with several applications, which are developed on the basis of material spectroscopy, radiative transfer, and imaging spectroscopy. HRS plays a vital role in agriculture for crops type classification and soil prediction. The recently developed artificial intelligence techniques can be used for crops type classification using HRS. This study develops an Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crop Type Classification (ISCO-DTLCTC) model. The ISCO-DTLCTC technique comprises initial preprocessing step to extract the region of interest. The information gain-based feature reduction technique is employed to reduce the dimensionality of the original hyperspectral images. In addition, a fusion of 3 deep convolutional neural networks models namely, VGG16, SqueezeNet, and Dense-EfficientNet perform feature extraction process. Furthermore, sine cosine optimization (SCO) algorithm with Modified Elman Neural Network (MENN) model is applied for crops type classification. The design of SCO algorithm helps to proficiently select the parameters involved in the MENN model. The performance validation of the ISCO-DTLCTC model is carried out using benchmark datasets and the results are inspected under several measures. Extensive comparative results demonstrated the betterment of the ISCO-DTLCTC model over the state of art approaches with maximum accuracy of 99.99%. RÉSUMÉLa t el ed etection hyperspectrale (HRS) est une technologie emergente et multidisciplinaire ayant plusieurs applications d evelopp ees sur la base de la spectroscopie des mat eriaux, du transfert radiatif et de la spectroscopie des images. L'HRS joue un rôle essentiel en agriculture pour la classification des types de cultures et la pr evision des sols. Les techniques d'intelligence artificielle (IA) r ecemment d evelopp ees peuvent être utilis ees pour la classification des types de cultures a l'aide de HRS. Cette etude d eveloppe un mod ele intelligent d'optimisation du sinus-cosinus avec une classification des types de cultures bas ee sur l'apprentissage par transfert profond (ISCO-DTLCTC). La technique ISCO-DTLCTC comprend une etape initiale de pr etraitement pour extraire la r egion d'int erêt (RoI). La technique IGFR (Information Gain Based Feature Reduction) est utilis ee pour r eduire la dimensionnalit e des images hyperspectrales originales. Une fusion de trois mod eles DCNN (Deep Convolutional Neural Networks), a savoir VGG16, SqueezeNet et Dense-EfficientNet, effectue un processus d'extraction des principales caract eristiques. En outre, l'algorithme d'optimisation du sinuscosinus (SCO) avec le mod ele MENN (Modified Elman Neural Network) est appliqu e a la classification des types de cultures. La conception de l'algorithme SCO permet de s electionner efficacement les param etres impliqu es dans le mod ele MENN. La validation des performances du mod ele ISCO-DTLCTC est effectu ee a l'aide d'ensembles de donn ees de r ef erence et

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“…which is attained by resolving the subsequent quadratic programming problem (QPP) provided in Eq. At this point, 𝑏 signifies the bias, 𝑤 ∈ ℱ stands for the weight vector of optimum hyperplane within the altered feature space ℱ, 𝜉 𝑖 implies the surplus variable representing the error connected to 𝑖 𝑡ℎ instance margin interms of the separate hyperplane [19], 𝐶 implies the regularization parameter that controls the misclassification cost of samples and penalizes 𝜉 𝑖 , 𝐶 > 0. With the overview of non-negative Lagrange multiplier 𝛼 𝑖 , Eq.…”

Section: Image Classificationmentioning

confidence: 99%

Intelligent bite marking analysis and classification using deep convolutional neural network based Xception model

Chandramouleeswaran

Puviarasan

2022

ijhs

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Bite mark analysis and classification play a vital role in forensics. The recent advances in computer vision and deep learning models paves a way for the design of automated bite mark detection and classification process. This article focuses on the design of intelligent bite marking analysis and classification using deep convolutional neural network based Xception model. The major goal of the proposed model is to determine the appropriate class labels for the bite marked images. The proposed model initially intends to pre-process the bite marked images in different ways such as hair removal, median filtering based noise removal, and adaptive histogram based contrast enhancement. Besides, Chan Vese Segmentation approach is applied for segmenting the bite marked images. The data augmentation process is performed for increasing the count of images. In addition, Xception model is employed for the extraction of features. Finally, two machine learning (ML) classifications such as support vector machine (SVM) and logistic regression (LR) models are employed for image classification. For demonstrating the enhanced performance of the presented models, a set of simulations were carried out on their own dataset and the results ensured the betterment of the proposed model over the other existing models.

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A hybrid ensemble-filter wrapper feature selection approach for medical data classification

Cited by 71 publications

References 71 publications

An Intelligent Hybrid Ensemble Gene Selection Model for Autism Using DNN

An Intelligent Hybrid Ensemble Gene Selection Model for Autism Using DNN

Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crops Type Classification Using Hyperspectral Images

Intelligent bite marking analysis and classification using deep convolutional neural network based Xception model

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