Selective classification considering time series characteristics for spiking neural networks

Abstract

In this paper, we propose new methods for estimating the relative reliability of prediction and rejection methods for selective classification for spiking neural networks (SNNs). We also optimize and improve the efficiency of the RC curve, which represents the relationship between risk and coverage in selective classification. Efficiency here means greater coverage for risk and less risk for coverage in the RC curve. We use the model internal representation when time series data is input to SNN, rank the prediction results that are the output, and reject them at an arbitrary rate. We propose multiple methods based on the characteristics of datasets and the architecture of models. Multiple methods, such as a simple method with discrete coverage and a method with continuous and flexible coverage, yielded results that exceeded the performance of the existing method, softmax response.