Optimizing Feature Relevance: A Deep Learning Perspective

Authors

Gaddam Kavyasri

Abstract

Feature Selection has turned into the main point of investigations particularly in bioinformatics where there are numerous applications. Deep learning technology is a useful asset to choose features; anyway, not all calculations are on an equivalent balance with regards to the selection of relevant features. To be sure, numerous techniques have been proposed to select multiple features using deep learning techniques. Because of deep learning, neural systems have profited a gigantic toprecovery in the previous couple of years. Anyway, neural systems are black-box models and not many endeavors have been made to examine the fundamental procedure. In this proposed work a new calculation to do feature selection with deep learning systems is introduced. To evaluate our outcomes, we create relapse and grouping issues that enable us to think about every calculation on various fronts: exhibitions, calculation time, and limitations. The outcomes acquired are truly encouraging since we figure out how to accomplish our objective by outperforming irregular backwoods exhibitions for each situation. The results prove that the proposed method exhibits better performance than the traditional methods.

Keywords

feature selection, deep learning, neural networks, preprocessing, data extraction.

Optimizing Feature Relevance: A Deep Learning Perspective. Gaddam Kavyasri. 2024. IJIRCT, Volume 10, Issue 3. Pages 1-8. https://www.ijirct.org/viewPaper.php?paperId=2412116