Hybrid Pattern Recognition Using a Combination of Different Features (이종 특징 결합을 이용한 하이브리드 패턴 인식 방법)

Sang-Il Choi (최상일)

Hybrid Pattern Recognition Using a Combination of Different Features

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2015, 20(11), pp.9-16
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

Sang-Il Choi ¹

¹단국대학교

Accredited

ABSTRACT

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

KEYWORDS

Patternclassification, Featureextraction, Feature selection, Hybridmethod, Discriminant analysis, Combinedfeatures

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65