Sciweavers

Share
MCS
2004
Springer

Multiple Classifiers System for Reducing Influences of Atypical Observations

9 years 2 months ago
Multiple Classifiers System for Reducing Influences of Atypical Observations
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 highdimensional 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).
Sarunas Raudys, Masakazu Iwamura
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where MCS
Authors Sarunas Raudys, Masakazu Iwamura
Comments (0)
books