An Efficient and Robust Machine learning Framework for High-Dimensional Data Exploration and Analysis

Abstract

High-dimensional data poses significant challenges in analysis and interpretation due to the abundance of features and potential noise. This research introduces an innovative and robust machine learning framework tailored to explore and analyze high-dimensional datasets efficiently. This study proposes an innovative computational framework utilizing numerical modeling to create an environment for exploring High-Dimensional Data (HDD) during gene expression analysis. The framework's design involves complex data analysis by assessing a learning model using a streamlined workflow and practical predictive analysis. The primary objective is to improve the computational efficiency of workflow modeling by optimizing intrinsic elements and enhancing feature selection performance, thereby positively impacting data clustering and learning stages. The system also conducts effective pre-processing of gene expression data, aiding in the accurate identification and selection of informative genes during training and classification phases.In our research, we conducted a comparative analysis of various methods, including RBF, MLP, and SVM, to develop a robust machine-learning-based gene expression analysis system.