Authors :
Vishwas Victor; Dr. Ragini Shukla
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/8kb9bmhw
Scribd :
http://tinyurl.com/4zc2s2e9
DOI :
https://doi.org/10.5281/zenodo.10438850
Abstract :
There are numerous approaches for
handling microarray gene expression data since new
feature selection techniques are constantly being
developed. To create a new subset of pertinent features,
feature selection (FS) is utilized to pinpoint the essential
feature subset. The model that used the informative
subset projected that a classification model generated
solely using this subset would have higher predicted
accuracy than a model developed using the whole
collection of attributes.
We offer an analytical approach for cancer
classification and developed a model using Support
Vector Machine as classifier and after that
Convolutional Neural Network in the aspect of Deep
Learning. The outcome received in the context of the
proposed model is very impressive and accurate.
Keywords :
Feature selection; Optimization;Classification; Support Vector Machine (SVM); Deep Learning; Machine Learning; Convolutional Neural Network (CNN).
There are numerous approaches for
handling microarray gene expression data since new
feature selection techniques are constantly being
developed. To create a new subset of pertinent features,
feature selection (FS) is utilized to pinpoint the essential
feature subset. The model that used the informative
subset projected that a classification model generated
solely using this subset would have higher predicted
accuracy than a model developed using the whole
collection of attributes.
We offer an analytical approach for cancer
classification and developed a model using Support
Vector Machine as classifier and after that
Convolutional Neural Network in the aspect of Deep
Learning. The outcome received in the context of the
proposed model is very impressive and accurate.
Keywords :
Feature selection; Optimization;Classification; Support Vector Machine (SVM); Deep Learning; Machine Learning; Convolutional Neural Network (CNN).
