TY - GEN
T1 - Applying XCS on time variant problem
T2 - 2011 3rd World Congress on Nature and Biologically Inspired Computing, NaBIC 2011
AU - Lee, Po Ming
AU - Hsiao, Tzu-Chien
PY - 2011
Y1 - 2011
N2 - Extended Classifier System (XCS) has been proved to be a fine classifier for pattern recognition tasks and was adopted as a popular research tool for several active research fields. During the progress of developing XCS, several versions of XCS such as XCS with real value attribute (XCSR), XCS with additional memory (XCSM) and parallel XCS (DXCS), XCS as function approximator (XCSF) have been proposed to meet the needs of real world applications. On the other hand, in the field of biomedical engineering and financial time series forecasting, data gathered is inherently time variant, while both of them are most active research fields nowadays, it would be valuable to gain more insights from how XCS works when encounter with time variant data. Hence, in this study we examined XCS's performance on time variant problem and proposed an alternative version of XCS based on simulating human nature that combing wild guessing on everything and careful reaction together by separating thinking and acting components in the design of XCS. The results showed that the new version XCS (97.11% accuracy rate in average) out performed traditional XCS (77.73% accuracy rate in average), by significance level of p <60; 0.0001 on time variant 6-multiplexer problem.
AB - Extended Classifier System (XCS) has been proved to be a fine classifier for pattern recognition tasks and was adopted as a popular research tool for several active research fields. During the progress of developing XCS, several versions of XCS such as XCS with real value attribute (XCSR), XCS with additional memory (XCSM) and parallel XCS (DXCS), XCS as function approximator (XCSF) have been proposed to meet the needs of real world applications. On the other hand, in the field of biomedical engineering and financial time series forecasting, data gathered is inherently time variant, while both of them are most active research fields nowadays, it would be valuable to gain more insights from how XCS works when encounter with time variant data. Hence, in this study we examined XCS's performance on time variant problem and proposed an alternative version of XCS based on simulating human nature that combing wild guessing on everything and careful reaction together by separating thinking and acting components in the design of XCS. The results showed that the new version XCS (97.11% accuracy rate in average) out performed traditional XCS (77.73% accuracy rate in average), by significance level of p <60; 0.0001 on time variant 6-multiplexer problem.
KW - Extended Classifier System
KW - Somatic Marker Hypothesis
KW - Time Variant Problem
UR - http://www.scopus.com/inward/record.url?scp=83755196558&partnerID=8YFLogxK
U2 - 10.1109/NaBIC.2011.6089616
DO - 10.1109/NaBIC.2011.6089616
M3 - Conference contribution
AN - SCOPUS:83755196558
SN - 9781457711237
T3 - Proceedings of the 2011 3rd World Congress on Nature and Biologically Inspired Computing, NaBIC 2011
SP - 348
EP - 352
BT - Proceedings of the 2011 3rd World Congress on Nature and Biologically Inspired Computing, NaBIC 2011
Y2 - 19 October 2011 through 21 October 2011
ER -