A Learning Algorithm for Multiple Rule Trees
نویسندگان
چکیده
It is one of the key problems for web based decision support systems to generate knowledge from huge database containing inconsistent information. In this paper, a learning algorithm for multiple rule trees (MRT) is developed, which is based on ID3 algorithm and rough set theory. MRT algorithm can quickly generate decision rules from inconsistent decision information tables. Both space and time complexities of MRT algorithm are just polynomial, while those of Skowron’s default decision rule generation algorithm are exponential. With the increasing of the number of records and core attributes of an information table, Skowron’s default algorithm needs more memory and time for generating rules than MRT algorithm. In some cases, Skowron’s default decision rule generation algorithm could not generate rules due to the lack of memory. It’s proved by our simulation experiment results that MRT algorithm is effective and valid.
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