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2016 | 16 | 1 |
Article title

An implementation of articial advisor for dynamic classication of objects

Content
Title variants
Languages of publication
EN
Abstracts
EN
The paper presents an original method of dynamic classication of objects from a new domain which lacks an expert knowledge. The method relies on analysis of attributes of objects being classied and their general quality Q, which is a combination of particular object's attributes. The method uses a test of normality as a basis for computing the reliability factor of the classication (rfc), which indicates whether the classication and the model of quality Q are reliable. There is no need to collect data about all objects before the classication starts and possibly the best objects ale selected dynamically (on-the-y) while data concerning consecutive objects are gathered. The method is implemented as a software tool called Articial Classication Adviser (ACA). Moreover, the paper presents a case study, where the best candidates for reghting mobile robot operators are selected.
Keywords
EN
rfc   ACA   database  
Year
Volume
16
Issue
1
Physical description
Dates
published
2016
online
04 - 10 - 2016
Contributors
References
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Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.ojs-doi-10_17951_ai_2016_16_1_40
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