Rule Generation and Selection with a Parallel Generalisation Architecture

Ross Peter CLEMENT

Rule Generation and Selection with a Parallel Generalisation Architecture

Ross Peter CLEMENT

Full Text Views

0

Cite this

Summary :

The BAMBOO algorithm is an expert system rule generating algorithm developed from the well-known C4 decision tree algorthm. Because BAMBOO's search is less restricted than C4's it usually finds simpler rules than C4. Both algorithms have problems with incomplete search and brittleness. These problems can be avoided by layering both algorithms together with other algorithms, generating independent rule sets and selecting a subset of rules to use in the final expert system. This learning strategy is referred to as parallel generalisation. Problems of search and brittleness are because the algorithms have a single fixed bias. By layering several algorithms together the effect is of a single algoritm selectively applying many heuristics. Because selecting rules is much easier than generating rules, the select procedure has its own parameterised bias. The layered algorithm is much more flexible than the single algorithms, in addition to generating more accurate and concise rule sets. Brittleness is avoided as when one algorithm produces a worst case rule set other algorithms generate better rules. Parallel generalisation can be improved by altering the algorithms to cooperate moer.

Publication: IEICE TRANSACTIONS on Information Vol.E74-D No.7 pp.2093-2099

Publication Date: 1991/07/25

Publicized

Online ISSN

DOI

Type of Manuscript: PAPER

Category: Artificial Intelligence and Cognitive Science

Authors

Ross Peter CLEMENT

Keyword

Cite this

Copy

Ross Peter CLEMENT, "Rule Generation and Selection with a Parallel Generalisation Architecture" in IEICE TRANSACTIONS on Information, vol. E74-D, no. 7, pp. 2093-2099, July 1991, doi: .
Abstract: The BAMBOO algorithm is an expert system rule generating algorithm developed from the well-known C4 decision tree algorthm. Because BAMBOO's search is less restricted than C4's it usually finds simpler rules than C4. Both algorithms have problems with incomplete search and brittleness. These problems can be avoided by layering both algorithms together with other algorithms, generating independent rule sets and selecting a subset of rules to use in the final expert system. This learning strategy is referred to as parallel generalisation. Problems of search and brittleness are because the algorithms have a single fixed bias. By layering several algorithms together the effect is of a single algoritm selectively applying many heuristics. Because selecting rules is much easier than generating rules, the select procedure has its own parameterised bias. The layered algorithm is much more flexible than the single algorithms, in addition to generating more accurate and concise rule sets. Brittleness is avoided as when one algorithm produces a worst case rule set other algorithms generate better rules. Parallel generalisation can be improved by altering the algorithms to cooperate moer.
URL: https://global.ieice.org/en_transactions/information/10.1587/e74-d_7_2093/_p

Copy

@ARTICLE{e74-d_7_2093,
author={Ross Peter CLEMENT, },
journal={IEICE TRANSACTIONS on Information},
title={Rule Generation and Selection with a Parallel Generalisation Architecture},
year={1991},
volume={E74-D},
number={7},
pages={2093-2099},
abstract={The BAMBOO algorithm is an expert system rule generating algorithm developed from the well-known C4 decision tree algorthm. Because BAMBOO's search is less restricted than C4's it usually finds simpler rules than C4. Both algorithms have problems with incomplete search and brittleness. These problems can be avoided by layering both algorithms together with other algorithms, generating independent rule sets and selecting a subset of rules to use in the final expert system. This learning strategy is referred to as parallel generalisation. Problems of search and brittleness are because the algorithms have a single fixed bias. By layering several algorithms together the effect is of a single algoritm selectively applying many heuristics. Because selecting rules is much easier than generating rules, the select procedure has its own parameterised bias. The layered algorithm is much more flexible than the single algorithms, in addition to generating more accurate and concise rule sets. Brittleness is avoided as when one algorithm produces a worst case rule set other algorithms generate better rules. Parallel generalisation can be improved by altering the algorithms to cooperate moer.},
keywords={},
doi={},
ISSN={},
month={July},}

Copy

TY - JOUR
TI - Rule Generation and Selection with a Parallel Generalisation Architecture
T2 - IEICE TRANSACTIONS on Information
SP - 2093
EP - 2099
AU - Ross Peter CLEMENT
PY - 1991
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E74-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 1991
AB - The BAMBOO algorithm is an expert system rule generating algorithm developed from the well-known C4 decision tree algorthm. Because BAMBOO's search is less restricted than C4's it usually finds simpler rules than C4. Both algorithms have problems with incomplete search and brittleness. These problems can be avoided by layering both algorithms together with other algorithms, generating independent rule sets and selecting a subset of rules to use in the final expert system. This learning strategy is referred to as parallel generalisation. Problems of search and brittleness are because the algorithms have a single fixed bias. By layering several algorithms together the effect is of a single algoritm selectively applying many heuristics. Because selecting rules is much easier than generating rules, the select procedure has its own parameterised bias. The layered algorithm is much more flexible than the single algorithms, in addition to generating more accurate and concise rule sets. Brittleness is avoided as when one algorithm produces a worst case rule set other algorithms generate better rules. Parallel generalisation can be improved by altering the algorithms to cooperate moer.
ER -