Title :
The performance of the DXCS system on continuous-valued inputs in stationary and dynamic environments
Author :
Dam, Hai H. ; Abbass, Hussein A. ; Lokan, Chris
Author_Institution :
Artificial Life & Adaptive Robotics Lab., New South Wales Univ., Canberra, ACT
Abstract :
XCS is widely accepted as one of the most reliable Michigan-style learning classifier system for data mining. Many studies found that XCS is able to provide good generalization using a ternary representation for binary inputs as well as interval representation for continuous-valued inputs. Since distributed data mining is becoming more popular due to massive data sets spread across a network at many organizations, we have proposed an XCS system for distributed data mining called DXCS. DXCS has been tested on binary inputs. The results showed that DXCS does not only achieve as good performance as the centralized XCS system, but also reduces data transmission in the network. In this paper, we further examine DXCS with real-valued inputs in stationary and dynamic environments
Keywords :
data mining; distributed databases; learning (artificial intelligence); pattern classification; DXCS system; Michigan-style learning classifier system; binary inputs; continuous-valued inputs; distributed data mining; dynamic environments; stationary environments; ternary representation; Australia; Data communication; Data mining; Data security; Database systems; Distributed databases; Distributed decision making; Machine learning; Robots; Testing;
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Conference_Location :
Edinburgh, Scotland
Print_ISBN :
0-7803-9363-5
DOI :
10.1109/CEC.2005.1554740
