.png){kind=icon}
Return
Attribute dependency functions considering data efficiency
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijar.2009.08.002.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Pawlak's attribute dependency degree model is applicable to feature selection in pattern recognition. However, the dependency degrees given by the model are often inadequately computed as a result of the indiscernibility relation. This paper discusses an improvement to Pawlak's model and presents a new attribute dependency function. The proposed model is based on decision-relative discernibility matrices and measures how many times condition attributes are used to determine the decision value by referring to the matrix. The proposed dependency degree is computed by considering the two cases that two decision values are equal or unequal. A feature of the proposed model is that attribute dependency degrees have significant properties related to those of Armstrong's axioms. An advantage of the proposed model is that data efficiency is considered in the computation of dependency degrees. It is shown through examples that the proposed model is able to compute dependency degrees more strictly than Pawlak's model. (C) 2009 Elsevier Inc. All rights reserved.
Keywords:
Rough sets
Reducts
Attribute dependency
Decision-relative discernibility matrix
Armstrong's axioms
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
3.0K
Citations:
5.1K
Organization
No organization information available
Cited Papers
Late gadolinium enhancement on cardiac magnetic resonance combined with 123I- metaiodobenzylguanidine scintigraphy strongly predicts long-term clinical outcome in patients with dilated cardiomyopathy.png)
PLOS ONEOAAI
PLOS ONEIF0
OAAIShareSaveShareSaveShareSave
ShareSaveShareSaveShareSaveShareSave
View more