Earticle

초록

영어

The real challenge in pattern recognition tasks and machine learning processes is to train a discriminator using labeled data and use it to distinguish between future data points as accurate as possible. However, most of the problems in the real world have numerous data. Therefore assigning labels to every data points in these problems are a cumbersome or even impossible matter. Semi-supervised learning is one approach to overcome these types of problems. It uses only a small set of labeled with the company of huge remain and unlabeled data to train the discriminator. In semi-supervised learning, it is very essential that which data is labeled and depend on position of data it effectiveness changes. In this paper, we proposed an evolutionary approach called Artificial Immune System (AIS) to determine which data is better to be labeled to get the high quality data. The experimental results represent the effectiveness of this algorithm in finding these data points.

목차

Abstract
 1. Introduction
 2. Human Immune System
  2.1 Primary Activation
  2.2 Secondary Activation
 3. aiNet Algorithm
 4. Proposed Algorithm
 5. Experimental Result
 6. Conclusion
 References

키워드

저자

• Amin Allahyar [ Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran ]
• Hadi Sadoghi Yazdi [ Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605