Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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Active Selection of Label Data for Semi-Supervised Learning Algorithm

준감독 학습 알고리즘을 위한 능동적 레이블 데이터 선택

  • Received : 2013.07.18
  • Accepted : 2013.08.21
  • Published : 2013.09.30
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Abstract

The choice of labeled data in semi-supervised learning algorithm can result in effects on the performance of the resultant classifier. In order to select labeled data required for the training of a semi-supervised learning algorithm, VCNN(Vector Centroid Neural Network) is proposed in this paper. The proposed selection method of label data is evaluated on UCI dataset and caltech dataset. Experiments and results show that the proposed selection method outperforms conventional methods in terms of classification accuracy and minimum error rate.

본 논문에서는 준감독 학습 알고리즘(Semi-Supervised Learning Algorithm)의 학습데이터에 필요한 소수의 레이블 데이터를 능동적으로 선택하기 위한 무감독경쟁학습 알고리즘인 VCNN(Vector Centroid Neural Network)을 제안한다. 준감독 학습 알고리즘에서 레이블 데이터의 선택은 학습 결과 큰 영향을 미치고, 레이블 데이터를 선택하는데 있어 많은 비용과 전문적인 지식이 필요하다. 본 논문에서 능동적이고 효율적인 레이블 데이터 선택을 검증하기 위하여 UCI database 와 caltech dataset 을 이용하여 실험한 결과, 기존의 레이블 데이터 선택 방법과 비교하여 안정된 분류 결과와 최소의 오차율을 나타냈다.

Keywords

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