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Alan H. Fielding

dblp:396/1497 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1987
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
species identification
0.011987
Expert systems: frames, rules or logic for species identification? · Comput. Appl. Biosci. 1987
Bioinformatics and computational biology
taxonomy
0.011987
Expert systems: frames, rules or logic for species identification? · Comput. Appl. Biosci. 1987

Methods — techniques the papers use, named apart from their topics

rule-based reasoning · 0.0probabilistic identification · 0.0frame-based representation · 0.0
YearPublicationVenuePosition
1987 Expert systems: frames, rules or logic for species identification?
abstract
The role of expert systems in species identification, with particular reference to the problems posed by damaged specimens and inexperienced taxonomists, is discussed. Of the three main types of expert systems available, the frame-based system is shown to provide the most appropriate model for a taxonomic expert system rather than a logic- or rule-based system. The advantages of an expert system over other computer-aided methods of identification are considered. A rule-based system requires the original knowledge (species descriptions) to be structured into rules, whereas a frame-based system can store the generic and specific descriptions in a series of frames. The frames fall into a hierarchy which closely resembles the taxonomic hierarchy, and down which information can be inherited. Two aspects of frame-based systems considered are the use of probabilities in identification, and the optimum structure of the knowledge base. The conventional use of probabilities is to provide an indication of the correctness of the result. However, in some studies involving the identification of many specimens, the speed of identification may be increased (with a reduction in accuracy) if identifications are made to a predetermined probability level. Although frames allow accurate representation of the taxonomic hierarchy, a semantic net, incorporating structures of the organism and/or details of the habitat may result in a more efficient expert system.
Marion Edwards, David R. Morse, Alan H. Fielding
Comput. Appl. Biosci.3