DC pole | Wartość | Język |
dc.contributor.author | Żabiński, Krzysztof | - |
dc.contributor.author | Zielosko, Beata | - |
dc.date.accessioned | 2020-12-31T06:45:30Z | - |
dc.date.available | 2020-12-31T06:45:30Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | "Entropy" Vol. 23, iss. 1 (2021), art. no. 14 | pl_PL |
dc.identifier.issn | 1099-4300 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12128/18096 | - |
dc.description.abstract | In the paper, an approach for decision rules construction is proposed. It is studied from
the point of view of the supervised machine learning task, i.e., classification, and from the point
of view of knowledge representation. Generated rules provide comparable classification results
to the dynamic programming approach for optimization of decision rules relative to length or
support. However, the proposed algorithm is based on transformation of decision table into entity–
attribute–value (EAV) format. Additionally, standard deviation function for computation of averages’
values of attributes in particular decision classes was introduced. It allows to select from the whole
set of attributes only these which provide the highest degree of information about the decision.
Construction of decision rules is performed based on idea of partitioning of a decision table into
corresponding subtables. In opposite to dynamic programming approach, not all attributes need
to be taken into account but only these with the highest values of standard deviation per decision
classes. Consequently, the proposed solution is more time efficient because of lower computational
complexity. In the framework of experimental results, support and length of decision rules were
computed and compared with the values of optimal rules. The classification error for data sets
from UCI Machine Learning Repository was also obtained and compared with the ones for dynamic
programming approach. Performed experiments show that constructed rules are not far from the
optimal ones and classification results are comparable to these obtained in the framework of the
dynamic programming extension. | pl_PL |
dc.language.iso | en | pl_PL |
dc.rights | Uznanie autorstwa 3.0 Polska | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/pl/ | * |
dc.subject | decision rules | pl_PL |
dc.subject | classification | pl_PL |
dc.subject | length | pl_PL |
dc.subject | support | pl_PL |
dc.subject | dynamic programming approach | pl_PL |
dc.subject | entity-attribute-value model | pl_PL |
dc.title | Decision rules construction : algorithm based on EAV model | pl_PL |
dc.type | info:eu-repo/semantics/article | pl_PL |
dc.identifier.doi | 10.3390/e23010014 | - |
Pojawia się w kolekcji: | Artykuły (WNŚiT)
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