Feature selection for high dimensional data in astronomy

TL;DR

This paper reviews feature selection methods for high-dimensional astronomical data, compares their performance and computational costs, and highlights the importance of choosing suitable methods and algorithms for effective data analysis.

Contribution

It provides a comprehensive taxonomy of feature selection methods and a case study comparing filter and wrapper approaches in astronomical data analysis.

Findings

Filter methods are computationally more efficient than wrapper methods.

Different learning algorithms combined with feature selection can improve performance.

Filter methods like ReliefF and Fisher filter perform well in high-dimensional settings.

Abstract

With an exponentially increasing amount of astronomical data, the complexity and dimension of astronomical data are likewise growing rapidly. Extracting information from such data becomes a critical and challenging problem. For example, some algorithms can only be employed in the low-dimensional spaces, so feature selection and feature extraction become important topics. Here we describe the difference between feature selection and feature extraction methods, and introduce the taxonomy of feature selection methods as well as the characteristics of each method. We present a case study comparing the performance and computational cost of different feature selection methods. For the filter method, ReliefF and fisher filter are adopted; for the wrapper method, improved CHAID, linear discriminant analysis (LDA), Naive Bayes (NB) and C4.5 are taken as learners. Applied on the sample, the result indicates that from the viewpoints of computational cost the filter method is superior to the wrapper method. Moreover, different learning algorithms combined with appropriate feature selection methods may arrive at better performance.