TY - JOUR
T1 - Parallel model refinement of inductive applications using method repository
AU - Abe, Hidenao
AU - Yamaguchi, Takahira
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2002
Y1 - 2002
N2 - Here is presented parallel CAMLET that is a platform for automatic composition of inductive applications with method repositories that organize many inductive learning methods. After having implemented CAMLET on a UNIX parallel machine with Perl and C languages, we have done the case studies of constructing inductive applications for eight different data sets from the StatLog repository. To find out an efficient search method, we have done the following experiments: a random search, a GA based search, and two hybrid searches with unifying each global search and the local search which uses meta-rules for refining a specification. That have shown us that the hybrid search works better than the other search methods. Furthermore, comparing the accuracy of inductive applications composed by parallel CMALET with that of popular twenty four inductive algorithms, we have shown that parallel CAMLET support a user in doing model selection in KDD engineering processes.
AB - Here is presented parallel CAMLET that is a platform for automatic composition of inductive applications with method repositories that organize many inductive learning methods. After having implemented CAMLET on a UNIX parallel machine with Perl and C languages, we have done the case studies of constructing inductive applications for eight different data sets from the StatLog repository. To find out an efficient search method, we have done the following experiments: a random search, a GA based search, and two hybrid searches with unifying each global search and the local search which uses meta-rules for refining a specification. That have shown us that the hybrid search works better than the other search methods. Furthermore, comparing the accuracy of inductive applications composed by parallel CMALET with that of popular twenty four inductive algorithms, we have shown that parallel CAMLET support a user in doing model selection in KDD engineering processes.
KW - Automatic composition
KW - Inductive applications
KW - Meta-learning
KW - Repository
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U2 - 10.1527/tjsai.17.647
DO - 10.1527/tjsai.17.647
M3 - Article
AN - SCOPUS:18444408693
VL - 17
SP - 647
EP - 657
JO - Transactions of the Japanese Society for Artificial Intelligence
JF - Transactions of the Japanese Society for Artificial Intelligence
SN - 1346-0714
IS - 5
ER -