Hybrid Pattern Recognition Using a Combination of Different Features

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Choi, Sang-Il

  • 투고 : 2015.08.21
  • 심사 : 2015.10.13
  • 발행 : 2015.11.30

초록

We propose a hybrid pattern recognition method that effectively combines two different features for improving data classification. We first extract the PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) features, both of which are widely used in pattern recognition, to construct a set of basic features, and then evaluate the separability of each basic feature. According to the results of evaluation, we select only the basic features that contain a large amount of discriminative information for construction of the combined features. The experimental results for the various data sets in the UCI machine learning repository show that using the proposed combined features give better recognition rates than when solely using the PCA or LDA features.

키워드

Pattern classification;Feature extraction;Feature selection;Hybrid method;Discriminant analysis;Combined features

참고문헌

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과제정보

연구 과제 주관 기관 : IITP(Institute for Information & communications Technology Promotion), National Research Foundation of Korea (NRF)