Combination of Designed Immune Based Classifiers for ERP Assessment in a P300-Based GKT

Constructing a precise classifier is an important issue in pattern recognition task. Combination the decision of several competing classifiers to achieve improved classification accuracy has become interested in many research areas. In this study, Artificial Immune System (AIS) as an effective artificial intelligence technique was used for designing of several efficient classifiers. Combination of multiple immune based classifiers was tested on ERP assessment in a P300-based Guilty Knowledge Test (GKT). Our experimental results showed that the proposed classifier named Compact Artificial Immune System (CAIS) is a successful classification method and can be competitive to other classifiers such as K-Nearest Neighbourhood (KNN), Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). Also, in the experiments, it was observed that using the decision fusion techniques for multiple classifier combination lead to better recognition results. The best rate of recognition by CAIS was 80.90% that has been improved in compare to other applied classification methods in our study.