CoySvM-(GeD): Coyote Optimization-Based Support Vector Machine Classifier for Cancer Classification Using Gene Expression Data

Reddy, S. Sai Satyanarayana; Kumar, Ashwani; Ghafoor, Kayhan Zrar; Bhardwaj, Ved Prakash; Manoharan, S.

doi:10.1155/2022/6716937

Cited by 6 publications

(4 citation statements)

References 33 publications

(43 reference statements)

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“…A cancer classification process was initiated by Reddy et al [22] using the SVM approach with microarray gene expression data. The Genetic material expression data belonging to independent tumour types are processed by an SVM classifier that increases the potentiality of genetic data.…”

Section: Literature Reviewmentioning

confidence: 99%

Gene Selection Based Cancer Classification With Adaptive Optimization Using Deep Learning Architecture

Das,

Neelima,

Deepa

et al. 2024

IEEE Access

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Early cancer identification using gene expression data is critical for providing successful patient care. Accurate data recognition is essential to prevent improper detection because it may result in higher complexities and increased mortality rates. Gene expression data typically include numerous features, each representing distinct genes. The abundance of features introduces high dimensionality, contributing to high computational complexity and resource demands. Furthermore, the presence of redundant or highly correlated selected features may lead to multicollinearity issues. In the existing works, certain limitations, such as reduced performance due to degraded data quality, high storage space requirements, overfitting issues, and lack of robustness, can compromise overall classification accuracy. To address these challenges and enhance classification outcomes, this research employs an efficient framework based on a deep learning (DL) approach. Initially, the data is collected from five gene cancer datasets, which are then augmented to maximize the data size. Min-Max Normalization is used for data pre-processing. The Enhanced Chimp Optimization (ECO) algorithm is applied to select the most significant genes while eliminating redundant or unwanted ones. Based on the selected gene set, the Depth-wise Separable Convolutional Neural Network (DSCNN) is employed to categorize diverse cancerous and non-cancerous classes. The proposed model's performance is highly beneficial by overcoming the existing issues of dimensionality and overfitting. The implementation is carried out using PYTHON, and the overall accuracy of the DSCNN model is determined to be 99.18% across all datasets. Additionally, metrics such as precision, recall, and F1 score are evaluated to analyze the model's overall performance. The proposed model demonstrates a significantly higher effective performance compared to existing approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Gene Selection Based Cancer Classification With Adaptive Optimization Using Deep Learning Architecture

Das,

Neelima,

Deepa

et al. 2024

IEEE Access

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“…An essential concept in statistical theory, the Vapnik-Chervonenkis dimension (VC dimension), can measure the generalization ability of the model trained by the support vector machine [16][17][18][19]. Under limited training samples, the larger the VC dimension of the learning machine, the more complex the learning machine will be, and the larger the confidence interval will be, which will eventually lead to a larger gap between the actual risk and the empirical risk, which means the model is more generalizable.…”

Section: Basic Eorymentioning

confidence: 99%

Optimization Design of Drilling Fluid Chemical Formula Based on Artificial Intelligence

Chen

2022

Computational Intelligence and Neuroscience

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Through the research and development of the regression prediction function of support vector machine, this paper applies it to the prediction of drilling fluid performance parameters and the formulation design of drilling fluid. The research in this paper can reduce the experimental workload and improve the efficiency of drilling fluid formulation design. The apparent viscosity (AV), plastic viscosity (PV), API filter loss (FLAPI), and roll recovery (R) of the drilling fluid were selected as the inspection objects of the drilling fluid performance parameters, and the support vector machine was used to establish a model for predicting the drilling fluid performance parameters. This predictive model was used as part of the overall drilling fluid formulation optimization design model. For a given drilling fluid performance parameter requirement, this model can be applied to reverse the addition of various treatment agents, and finally, the prediction accuracy of the model is verified by experiments.

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“…The major reason behind searching for effective approaches is to predict the survival rates to grab better treatment. The feature selection approaches are highly efficient in eradicating the noisy features, redundant data and are significant in describing the biological features when minimizing the model complexity [14]. The chief focus of the feature selection approach is to reduce the data dimensionality, which improves the overall system performance [15]- [16].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multiple Indefinite Kernel Learning Framework for Disease Classification from Gene Expression Data

Swetha¹,

Srinivasan²,

P³

2023

IJACSA

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In recent years, Machine Learning (ML) techniques have been used by several researchers to classify diseases using gene expression data. Disease categorization using heterogeneous gene expression data is often used for defining critical problems such as cancer analysis. A variety of evaluated factors known as genes are used to characterize the gene expression data gathered from DNA microarrays. Accurate classification of genetic data is essential to provide accurate treatments to sick people. A large number of genes can be viewed simultaneously from the collected data. However, processing this data has some limitations due to noises, redundant data, frequent errors, increased complexity, smaller samples with high dimensionality, difficult interpretation, etc. A model must be able to distinguish the features in such heterogeneous data with high accuracy to make accurate predictions. So this paper presents an innovative model to overcome these issues. The proposed model includes an effective multiple indefinite kernel learning based model for analyze the gene expression microarray data, then an optimized kernel principal component analysis (OKPCA) to select best features and hybrid flow-directed arithmetic support vector machine (SVM)-based multiple infinite kernel learning (FDASVM-MIKL) model for classification. Flow direction and arithmetic optimization algorithms are combined with SVM to increase classification accuracy. The proposed technique has an accuracy of 99.95%, 99.63%, 99.60%, 99.51%, and 99.79% using the datasets including colon, Isolet, ALLAML, Lung_cancer, and Snp2 graph.

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CoySvM-(GeD): Coyote Optimization-Based Support Vector Machine Classifier for Cancer Classification Using Gene Expression Data

Cited by 6 publications

References 33 publications

Gene Selection Based Cancer Classification With Adaptive Optimization Using Deep Learning Architecture

Gene Selection Based Cancer Classification With Adaptive Optimization Using Deep Learning Architecture

Optimization Design of Drilling Fluid Chemical Formula Based on Artificial Intelligence

A Hybrid Multiple Indefinite Kernel Learning Framework for Disease Classification from Gene Expression Data

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