Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/15832
Title: Machine learning based classification system for overlapping data and irregular repetititve signals
Authors: SIT WING YEE
Keywords: fuzzy ARTMAP, multiple classes, expert knowledge
Issue Date: 27-Mar-2009
Source: SIT WING YEE (2009-03-27). Machine learning based classification system for overlapping data and irregular repetititve signals. ScholarBank@NUS Repository.
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.
URI: http://scholarbank.nus.edu.sg/handle/10635/15832
Appears in Collections:Master's Theses (Open)

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