An automated classification algorithm for multi-wavelength data

The important step of data preprocessing of data mining is feature selection. Feature selection is used to improve the performance of data mining algorithms by removing the irrelevant and redundant features. By positional cross-identification, the multi-wavelength data of 1656 active galactic nuclei (AGNs), 3718 stars, and 173 galaxies are obtained from optical (USNO-A2.0), X-ray (ROSAT), and infrared (Two Micron AllSky Survey) bands. In this paper we applied a kind of filter approach named ReliefF to select features from the multi-wavelength data. Then we put forward the naive Bayes classifier to classify the objects with the feature subsets and compare the results with and without feature selection, and those with and without adding weights to features. The result shows that the naive Bayes classifier based on ReliefF algorithms is robust and efficient to preselect AGN candidates.