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論文の言語 英語
著者 Sarunas Raudys and Masakazu Iwamura
論文名 Multiple Classifiers System for Reducing Influences of Atypical Observations
論文誌名 Fifth International Workshop on Multiple Classifier Systems (MCS 2004)
Vol. 3077
ページ pp.233-242
年月 2004年6月
要約 Atypical observations, which are called outliers, are one of difficulties to apply standard Gaussian density based pattern classification methods. Large number of outliers makes distribution densities of input features multimodal. The problem becomes especially challenging in high-dimensional feature space. To tackle atypical observations, we propose multiple classifiers systems (MCSs) whose base classifiers have different representations of the original feature by transformations. This enables to deal with outliers in different ways. As the base classifier, we employ the integrated approach of statistical and neural networks. This consists of data whitening and training of single layer perceptron (SLP). Data whitening makes marginal distributions close to unimodal, and SLP is robust to outliers. Various kinds of combination strategies of the base classifiers achieved reduction of generalization error in comparison with the benchmark method, the regularized discriminant analysis (RDA).