Data Selection for Semi-Supervised Learning
The real challenge in pattern recognition task and machine learning process is to train a discriminator using labeled data and use it to distinguish between future data as accurate as possible. However, most of the problems in the real world have numerous data, which labeling them is a cumbersome or even an 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.
Keywords: machine learning, semi-supervised learning, evolutionary algorithm, artificial immune system, data selection
Download Full-Text
ABOUT THE AUTHORS
Shafigh Parsazad
Shafigh Parsazad is graduated from Ferdowsi University of Mashhad, Iran in computer engineering and is graduated from Ferdowsi University of Mashhad, Mashhad, Iran in Artificial Intelligence. He is interested in “Pattern Recognition”, “Image Processing”, “Network Security” and “Bioinformatics”. He is currently working on Nature-inspired algorithms and Computational Biology.
Ehsan Saboori
Ehsan Saboori is graduated from Ferdowsi University of Mashhad, Iran in computer engineering and is graduated from K.N Toosi University of technology, Tehran, Iran in IT engineering. He is interested in “Peer-to-Peer Networks”, “Computer Networks”, “Network Security” and “Anonymity”. He currently works on peer to peer network security and privacy.
Amin Allahyar
Department Of Computer Engineering, Ferdowsi University of Mashhad Mashhad, Iran
Shafigh Parsazad
Shafigh Parsazad is graduated from Ferdowsi University of Mashhad, Iran in computer engineering and is graduated from Ferdowsi University of Mashhad, Mashhad, Iran in Artificial Intelligence. He is interested in “Pattern Recognition”, “Image Processing”, “Network Security” and “Bioinformatics”. He is currently working on Nature-inspired algorithms and Computational Biology.
Ehsan Saboori
Ehsan Saboori is graduated from Ferdowsi University of Mashhad, Iran in computer engineering and is graduated from K.N Toosi University of technology, Tehran, Iran in IT engineering. He is interested in “Peer-to-Peer Networks”, “Computer Networks”, “Network Security” and “Anonymity”. He currently works on peer to peer network security and privacy.
Amin Allahyar
Department Of Computer Engineering, Ferdowsi University of Mashhad Mashhad, Iran
