Feature Selection Applied to Data from the Sloan Digital Sky Survey

In recent years there has been an explosion in the rate of acquisition of astronomical data. The analysis of astronomical data presents unprecedented opportunities and challenges for data mining in tasks, such as clustering, object discovery and classification. In this work, we address the feature selection problem in classification of photometric and spectroscopic data collected from the SDSS survey. We present a comparison of five feature selection algoritms: best first (BF), scatter search (SS), genetic algorithm (GA), best incremental ranked subset (BI) and best agglomerative ranked subset (BA). Up to now all these strategies were first applied to this paper to study relevant features in SDSS data.