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Proceedings Paper

An automated classification algorithm for multiwavelength data
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Paper Abstract

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 All- Sky 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.

Paper Details

Date Published: 16 September 2004
PDF: 8 pages
Proc. SPIE 5493, Optimizing Scientific Return for Astronomy through Information Technologies, (16 September 2004); doi: 10.1117/12.550982
Show Author Affiliations
Yan-Xia Zhang, National Astronomical Observatories (China)
A-Li Luo, National Astronomical Observatories (China)
Yong-Heng Zhao, National Astronomical Observatories (China)

Published in SPIE Proceedings Vol. 5493:
Optimizing Scientific Return for Astronomy through Information Technologies
Peter J. Quinn; Alan Bridger, Editor(s)

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