An Interpretable Fuzzy DBN-Based Classifier for Indoor User Movement Prediction in Ambient Assisted Living Applications

Abstract

In this paper, an interpretable fuzzy deep belief network (DBN)-based classifier called deep belief networks-based Takagi-Sugeno-Kang fuzzy classifier (DBN-TSK-FC) is created for indoor user movement prediction in ambient assisted living applications. With its promising classification performance, DBN-TSK-FC features sharing both the powerful neural representation ability of a DBN and the strong uncertainty-handling capability of an interpretable fuzzy representation. On the one hand, DBN-TSK-FC builds its interpretable fuzzy representation in a hierarchical way by applying the classical fuzzy clustering algorithm FCM to obtain fuzzy partitions on the training dataset. Then, it forms interpretable antecedent parts of fuzzy rules as the corresponding fuzzy representation. On the other hand, DBN-TSK-FC builds its DBN-based neural representation in the other hierarchical way. That is, it applies the existing unsupervised DBN pretraining on the training dataset and then takes the neural representation of all the hidden nodes in the top layer of the corresponding DBN as the set of consequent variables of fuzzy rules. In this approach, both the interpretable fuzzy representation and the DBN-based neural representation are further fused to form the corresponding fuzzy rules quickly by using the least learning machine (LLM) on both the fuzzy rules and the labeling information of the original dataset. Therefore, DBN-TSK-FC is essentially a deep TSK fuzzy classifier from the perspective of fuzzy rules, and it indeed avoids the very slow fine-tuning training required after the unsupervised pretraining of the existing DBN learning. The experimental results on the MovementAAL_RSS dataset indicate the effectiveness of the proposed classifier DBN-TSK-FC.