Machine learning based classification system for overlapping data and irregular repetititve signals

Abstract

Two classification modules in an overall system are looked into - one that does classification for data from overlapping classes using the fuzzy adaptive resonance theory map (fuzzy ARTMAP), and another which sorts repetitive signals, separating them into their respective sources. When faced with overlapping data, fuzzy ARTMAP suffers from the category proliferation problem on top of a difficulty in classification. These are overcome by a combination of modifications which allows multiple class predictions for certain data, and prevents the excessive creation of categories. Signal sorting methods such as sequence search and histogram methods can sort the signals into their respective sequences with a regular interval between signals, but effectiveness of the methods is affected when the intervals between signals in the source are highly deviating. Using available expert knowledge, the signals are effectively and accurately separated into their respective sources.