Ensemble Feature Selection in Scientific Data Analysis

Abstract

Feature selection has emerged as an important method of both examining and predicting results from scientific experiments. However, current feature selection methods cannot consistently provide meaningful descriptions of feature importance in the complex, high-dimensional, natural datasets encountered in scientific data analysis. Here, an ensemble feature selection method is demonstrated to collectively reduce the bias associated with individual feature selection algorithms and provide uncertainty estimates on feature importance ranks for both synthetic and natural datasets.