Interpretability of learning in a signal processing system

Abstract

The paper presents a software package that allows one to generate algorithms for automatic classification of signals. The software package includes an algorithm that converts records of continuous signals into vector form, a set of machine learning methods, as well as data mining tools aimed at achieving transparency and interpretability of learning. The approach is based on the presentation of differences between compared classes as a set of relatively simple, statistically significant and interpretable effects, which are graphically represented on two-dimensional diagrams. The performance of the method is illustrated on the problem of assessing the state of the hive by sound signals. The software package can be used in solving applied problems of automatic diagnostics and data analysis.