Abstract
Classification of different kinds of space objects plays an important role in many astronomy areas. Nowadays the classification process can possibly involve a huge amount of data. It could take a long time for processing and demand many resources for computation and storage. In addition, it may also take much effort to train a qualified expert who needs to have both the astronomy domain knowledge and the capability to manipulate the data. This research intends to provide an efficient, scalable classification system for astronomy research. We implement a dynamic classification framework and system using support vector machines (SVMs). The proposed system is based on a large-scale, distributed storage environment, on which scientists can design their analysis processes in a more abstract manner, instead of an awkward and time-consuming approach which searches and collects related subset of data from the huge data set. The experimental results confirm that our system is scalable and efficient.
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References
SDSS: Sloan digital sky survey (2009), http://www.sdss.org
PPDG: Particle physics data grid project (2009), http://www.ppdg.net
Austin, J., Davis, R., Fletcher, M., Jackson, T., Jessop, M., Liang, B., Pasley, A.: Dame: Searching large data sets within a grid-enabled engineering application. In: Proceedings of the IEEE, March 2005, pp. 496–509 (2005)
Ranawana, R., Palade, V.: Multi-classifier systems: Review and a roadmap for developers. International Journal of Hybrid Intelligent Systems 3(1), 35–61 (2006)
Woods, K., Kegelmeyer Jr., W.P., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(4), 405–410 (1997)
Zhu, X., Wu, X., Yang, Y.: Effective classification of noisy data streams with attribute-oriented dynamic classifier selection. Knowledge and Information Systems 9(3), 339–363 (2006)
Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Processing Letters 9(3), 293–300 (1999)
Kim, E., Ko, J.: Dynamic classifier integration method. Multiple Classifier Systems, 97–107 (2005)
Tran, Q.A., Zhang, Q.L., Li, X.: Reduce the number of support vectors by using clustering techniques. Machine Learning and Cybernetics 2, 1245–1248 (2003)
Woodsend, K., Gondzio, J.: High-performance parallel support vector machine training. Parallel Scientific Computing and Optimization 27, 83–92 (2008)
Hush, D., Scovel, C.: Polynomial-time decomposition algorithms for support vector machines. Machine Learning 51(1), 51–71 (2003)
Collobert, R., Bengio, S., Bengio, Y.: A parallel mixture of svms for very large scale problems. Neural Computation 14(5), V1105–V1114 (2002)
Chang, E., Zhu, K., Wang, H., Bai, H., Li, J., Qiu, Z., Cui, H.: Psvm: Parallelizing support vector machines on distributed computers. In: Advances in Neural Information Processing Systems, vol. 20 (2007)
Meligy, A., Al-Khatib, M.: A grid-based distributed svm data mining algorithm. European Journal of Scientific Research 27(3), 313–321 (2009)
Tang, C.H., Yu, C.H., Shen, C.H., Tsai, M., Wang, W.J., Chang, Z.W., Chen, W.P.: A system design for terabyte-scale, distributed multidimensional data management and analysis in the taos project. In: Proceedings for the HPC Asia and APAN 2009, pp. 336–342 (2009)
Nguyen, T.M.: Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval DevelopmentXInnovative Methods and Applications. Information Science Reference (2009)
pan STARRS: The panoramic survey telescope and rapid response system (2009), http://pan-STARRS.ifa.hawaii.edu
Bendjoya, Philippe, Zappal: Asteroid family identification. in asteroids iii, pp. 613–618. University of Arizona Press (2002)
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Tang, CH. et al. (2010). Efficient Astronomical Data Classification on Large-Scale Distributed Systems. In: Bellavista, P., Chang, RS., Chao, HC., Lin, SF., Sloot, P.M.A. (eds) Advances in Grid and Pervasive Computing. GPC 2010. Lecture Notes in Computer Science, vol 6104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13067-0_45
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DOI: https://doi.org/10.1007/978-3-642-13067-0_45
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