Abstract
A Fast Outlier Sample Detection(FOSD) algorithm is proposed in this paper which can be used to recognize mislabeled samples or abnormal samples in microarray datasets. The proposed algorithm uses CL-stability alorithm as a basic operator. The Machine Learning method is used as classifier in the FOSD. The outlier samples are detected depending on the gobal stability of samples. Experimental results show that the FOSD algorithm is not only better than other existing algorithms, but also robust for detecting outlier samples in microarray dataset.
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Zhou, Y., Xing, C., Shen, W., Sun, Y., Wu, J., Zhou, X. (2011). A Fast Algorithm for Outlier Detection in Microarray. In: Lin, S., Huang, X. (eds) Advances in Computer Science, Environment, Ecoinformatics, and Education. CSEE 2011. Communications in Computer and Information Science, vol 215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23324-1_83
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DOI: https://doi.org/10.1007/978-3-642-23324-1_83
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