OFVSD: a Python package of optimized forward variable selection decoder for high-dimensional neuroimaging data.

ABSTRACT: The complexity and high dimensionality of neuroimaging data pose problems for decoding information with machine learning (ML) models because the number of features is often much larger than the number of observations. Feature selection is one of the crucial steps for determining meaningful target features in decoding; however, optimizing the feature selection from such high-dimensional neuroimaging data has been challenging using conventional ML models. Here, we introduce an efficient and high-performance decoding package incorporating a forward variable selection (FVS) algorithm and hyper-parameter optimization that automatically identifies the best feature pairs for both classification and regression models, where a total of 18 ML models are implemented by default. First, the FVS algorit

SUBMITTER: Dang T 

PROVIDER: S-EPMC10566623 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature