Title: Don't Fear the Bit Flips: Optimized Coding Strategies for Binary Classification,
Abstract: After being trained, classifiers must often operate on data that has been
corrupted by noise. In this paper, we consider the impact of such noise on the
features of binary classifiers. Inspired by tools for classifier robustness, we
introduce the same classification probability (SCP) to measure the resulting
distortion on the classifier outputs. We introduce a low-complexity estimate of
the SCP based on quantization and polynomial multiplication. We also study
channel coding techniques based on replication error-correcting codes. In
contrast to the traditional channel coding approach, where error-correction is
meant to preserve the data and is agnostic to the application, our schemes
specifically aim to maximize the SCP (equivalently minimizing the distortion of
the classifier output) for the same redundancy overhead.