A hybrid prediction model integrating FCM clustering algorithm with supervised learning

Abstract

Since most prediction models are still using the algorithm based on supervised learning, they are flawed by various weaknesses, such as the issue of preprocessing the training data, and difficulties in applying them to new patterns other than the trained data. On the other hand, the prediction model that uses only unsupervised learning is flawed by its difficulty in analyzing the result of prediction because no information about the data is given as to when learning is conducted. In this paper, we propose a hybrid prediction model which integrates the FCM clustering algorithm belonging to unsupervised learning with the features of supervised learning that lead to collection of target values. The proposed hybrid prediction model conducts automatic classification without external interference, detects target values inside the data alone, and applies them to deriving numerical prediction results. Thus the proposed model possesses the strong features of both supervised learning and unsupervised learning. We performed a prediction using the actual measurement data in the ITS, and confirmed the accuracy of the result. We expect that the proposed hybrid prediction model may contribute to enhancement of automation standards in various intelligent systems.