Title: A Novel Approach to Feature Selection in High-Dimensional Data. 

Authors: Dr.  Mackenzie

Abstract: High-dimensional data, characterized by a large number of features compared to the number of samples, poses significant challenges for machine learning.  The presence of irrelevant or redundant features can degrade model performance, increase computational complexity, and hinder interpretability. Feature selection aims to identify the most informative subset of features, improving model accuracy and efficiency. This paper proposes a novel approach to feature selection in high-dimensional data, combining [ mention the core ideas of your approach, e.g., filter methods based on information theory with embedded methods using deep learning ].  Our method leverages [ mention the key strengths or benefits of your method, e.g., the ability to capture non-linear relationships and interactions between features while also providing a computationally efficient solution ]. We evaluate the performance of our proposed method on several benchmark high-dimensional datasets, comparing it with existing feature selection techniques.  Our results demonstrate that the proposed approach achieves [ mention the key results, e.g., superior performance in terms of accuracy, F1-score, and feature subset size ].  We further analyze the selected features to gain insights into the underlying data structure and discuss the implications of our findings.

Keywords: Feature Selection, Dimensionality Reduction, Machine Learning

International Journal of Applied Pattern Recognition, Vol. 6, No. 2, 2018 (Special Issue)

Received: 12 Feb 2018

Accepted: 24 April 2018

Published online: 01 June 2018