  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.
