Born-again tsk fuzzy classifier based on knowledge distillation

Abstract

This application provides a born-again TSK fuzzy classifier based on knowledge distillation. The born-again TSK fuzzy classifier based on knowledge distillation is denoted as CNNBaTSK, and a fuzzy rule of CNNBaTSK includes two parts: an antecedent part based on soft label information and a consequent part based on original data. A method for constructing the fuzzy rule of CNNBaTSK includes following steps: Step 1: taking, by the CNNBaTSK, the original data as input, obtaining a probability distribution of an output layer through a layer-by-layer neural expression, and introducing a distillation temperature to generate soft label information of DATASET; Step 2: partitioning the soft label information into five fixed fuzzy partitions to construct the fuzzy rule in a fuzzy part of the CNNBaTSK; Step 3: introducing the original data to calculate a consequent parameter, and optimizing the consequent parameter of CNNBaTSK using a non-iterative learning method.

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