Study on Stellar Spectral Outliers Mining Based on Fuzzy Large Margin and Minimum Ball Classification Model
LIU Zhong-bao1, ZHAO Wen-juan2
1. School of Electronics and Computer Science Technology, North University of China, Taiyuan 030051, China 2. School of Information, Business College of Shanxi University, Taiyuan 030031, China
Abstract：It’s one of the main goals in universe exploration to find unknown and special celestial bodies. The spectra outlier data is analyzed based on the traditional classification approaches, which is a general method of special celestial body exploration. But it’s depressed that many traditional classification approaches are insensitive to the outlier data, which even influence the classification efficiencies, therefore, these methods can’t accomplish the task of special celestial body exploration. In view of this, Fuzzy Large Margin and Minimum Ball Classification Model (FLM-MBC) is proposed in this paper. In FLM-MBC, part of general data and outlier data are trained to construct the minimum ball model and the fuzzy technique is introduced to reduce the noise influence to classification. Comparative experiments with C-SVM, KNN, and SVDD on the SDSS spectral datasets verify the effectiveness of the proposed FLM-MBC.
Key words：Stellar spectrum;Classification;Fuzzy large margin and minimum ball;Spectra outlier data
刘忠宝1，赵文娟2 . 基于模糊大间隔最小球分类模型的恒星光谱离群数据挖掘方法 [J]. 光谱学与光谱分析, 2016, 36(04): 1245-1248.
LIU Zhong-bao1, ZHAO Wen-juan2 . Study on Stellar Spectral Outliers Mining Based on Fuzzy Large Margin and Minimum Ball Classification Model . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(04): 1245-1248.
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