Abstract:

To obtain a classifier with good classification accuracy and interpretability, a deep fuzzy classifier based on feature transform and reconstruction (FR-DFC) is proposed. In FR-DFC, several fuzzy systems (FT_FS) for feature transform and a multi-prototype fuzzy classification system (MPRFD_FS) are stacked together to realize the classification process of the model, based on the hierarchically stacked thought originated from deep learning. Specifically, the stacked FT_FSs explore the hidden features in the data by transferring data from the original data space to the high-level feature space. MPRFD_FS, on the other hand, implements classification based on multiple prototypes that characterize the distribution of classifications in the high-level feature space. In addition, the proposed FR-DFC uses several fuzzy systems (RE_FS) for feature reconstruction to establish the mapping relationship between the high-level feature space and the original data space and establishes an understandably approximate fuzzy classifier in the original data space to ensure the interpretability of FR-DFC. Besides, FR-DFC utilizes gradient descent-based and end-to-end learning patterns to optimize the parameters of the model. The optimized objective function contains a classification loss function and a reconstruction loss function, which ensures both classification accuracy and interpretability of the model. Experimental results demonstrate that FR-DFC not only improves the classification accuracy but also possesses interpretability.

Key words: fuzzy system, feature transform, classification, interpretability, reconstruction