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Messy Coding in the XCS Classifier System for Sequence Labeling

Masaya Nakata, Tim Kovacs, Keiki Takadama, Messy Coding in the XCS Classifier System for Sequence Labeling. Parallel Problem Solving from Nature - PPSN XIII - 13th International Conference. ISBN 978-3-319-10761-5, pp. 191–200. September 2014. PDF, 173 Kbytes.

Abstract

The XCS classifier system for sequence labeling (XCS-SL) is an extension of XCS for sequence labeling, a form of time-series clas- sification where every input has a class label. In XCS-SL a classifier condition consists of some sub-conditions which refer back to previous inputs. Each sub-condition is a memory. A condition has n sub-conditions which represent an interval from the current time t0 to a previous time t−n. A problem of this representation (called interval coding) is, even if only one input at t−n is needed, the condition must consist of n sub- conditions to refer to it. We introduce a messy coding based condition where each sub-condition messily refers to a single previous time. Unlike the original coding, the set of sub-conditions does not necessarily rep- resent an interval, so it can represent compact conditions. The original XCS-SL evolutionary mechanism cannot be used with messy coding and our main innovation is a novel evolutionary mechanism. Results on a benchmark show that, compared to the original interval coding, messy coding results in a smaller population size and does not require as high a population size limit. However, messy coding requires more training with a high population size limit. On a real world sequence labeling task messy coding evolved a achieved higher accuracy with a smaller population size than the original interval coding.

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