Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Design of Optimized Radial Basis Function Neural Networks Classifier with the Aid of Principal Component Analysis and Linear Discriminant Analysis

주성분 분석법과 선형판별 분석법을 이용한 최적화된 방사형 기저 함수 신경회로망 분류기의 설계

  • Kim, Wook-Dong (Department of Electrical Engineering, The University of Suwon) ;
  • Oh, Sung-Kwun (Department of Electrical Engineering, The University of Suwon)
  • Received : 2012.03.16
  • Accepted : 2012.03.23
  • Published : 2012.12.25
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Abstract

In this paper, we introduce design methodologies of polynomial radial basis function neural network classifier with the aid of Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA). By minimizing the information loss of given data, Feature data is obtained through preprocessing of PCA and LDA and then this data is used as input data of RBFNNs. The hidden layer of RBFNNs is built up by Fuzzy C-Mean(FCM) clustering algorithm instead of receptive fields and linear polynomial function is used as connection weights between hidden and output layer. In order to design optimized classifier, the structural and parametric values such as the number of eigenvectors of PCA and LDA, and fuzzification coefficient of FCM algorithm are optimized by Artificial Bee Colony(ABC) optimization algorithm. The proposed classifier is applied to some machine learning datasets and its result is compared with some other classifiers.

본 연구에서는 주성분 분석법 및 선형 판별 분석법을 이용한 다항식 방사형 기저 함수 신경회로망 분류기의 설계 방법론을 소개한다. 주성분 분석법과 선형판별 분석법을 사용하여 주어진 데이터의 정보 손실을 최소화한 특징데이터를 생성하고 이를 다항식 방사형 기저함수 신경회로망의 입력데이터로 사용한다. 방사형 기저 함수 신경회로망의 은닉층은 FCM 클러스터링 알고리즘으로 구성되며 연결가중치는 1차 선형식을 사용하였다. 최적의 분류기 설계를 위해서 최근에 제안된 Artificial Bee Colony(ABC) 최적화 알고리즘을 사용하여 구조 및 파라미터를 동조하였다. ABC 알고리즘을 통해 주성분 분석법과 선형판별 분석법의 고유벡터의 수 및 FCM 클러스터링 알고리즘의 퍼지화 계수등의 파라미터를 동조한다. 제안된 분류기는 대표적인 Machine Learning(ML) 데이터를 사용하여 성능을 평가하며 기존 분류기와 성능을 비교한다.

Keywords

Acknowledgement

Supported by : 한국연구재단

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