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A Neural Network Classification Model Based on Covering and Immune Clustering Algorithm

Inspired by the information processing mechanism of the human brain, the artificial neural network (ANN) is a classic data mining method and a powerful soft computing technique. The ANN provides a valuable tool for information processing and pattern recognition, thanks to its advantages in distributed storage, parallel processing, fast problem-solving and adaptive learning. The constructive neural network (CNN) is a popular emerging neural network model suitable for processing largescale data. In this paper, a novel neural network classification model was established based on the covering algorithm (CA) and the immune clustering algorithm (ICA). The CA is highly comprehensible, and enjoys fast computing speed, and high recognition rate. However, the learning effect of this algorithm is rather poor, because the training set is randomly selected from the original data, and the number of nodes (covering number) and area being covered are greatly affected by the learning sequence. To solve the problem, the ICA was introduced to preprocess the data samples, and calculate the cluster centers based on the antibody-antigen affinity. The CA and the ICA work together to determine the covering center and radius automatically, and convert them into the weights and thresholds of the hidden layer of neural network. The number of hidden layer neurons equals the number of covering. In addition, the McCulloch-Pitts (M-P) neurons were adopted for the output layer. Based on the input feature of the hidden layer, the output feature completes the mapping from input to output, creating the final classes of the original data. The introduction of the ICA fully solves the defect of the CA. Finally, our neural network classification model was verified through experiments on real-world datasets.