Artificial Immune-Based Semi-supervised Learning for Classification

Recently, artificial immune algorithms have attracted great attention of researchers and been widely used in function optimization, pattern recognition and classification. Traditional artificial immune algorithms for classification are applied to supervised learning problems, which require completely labeled data for training models. However, in many real-world scenarios, it is difficult to obtain all labeled samples. To solve this problem, an efficient semi-supervised artificial immune algorithm for classification tasks is proposed. It employs a clonal selection algorithm to generate memory cells used for classification, which is achieved via selection, cloning, and mutation procedures. Moreover, it utilizes the ensemble learning technique to extend the co-training paradigm and improves classification performance by adding the most confident unlabeled samples into the labeled set. In addition, the theory of learning from noisy examples is adopted to decide whether there are enough newly labeled samples that are used to reduce the negative effects caused by noises. Experimental results show that the proposed method achieves better or comparable performance than well-known semi-supervised and supervised methods on four datasets.