Brain Emotional Learning for Classification Problem - Reza Mahdi Hadi; Saeed Shokri; Omid Sojodishijani (Institute of Advanced Engineering and Science, 2014)
Emotional learning is new tool in the field of machine learning that the inspired from limbic system. The various models of emotional learning (BEL) have been successfully utilized in many learning problems. For example, control applications and prediction problems. In this paper a new architecture based on a brain emotional learning model that can be used in classification problem (BELC). This model is suitable for high dimensional classification applications. To evaluate the proposed method have been compare it with the Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN), Naive Bayes classifier and Brain Emotional Learning-Based Pattern Recognizer (BELPR) methods. The obtained results show the effectiveness and efficiency of the proposed method for classification problems.