An implementation of articial advisor for dynamic classication of objects

Barbara Łukawska, Grzegorz Łukawski, Krzysztof Sapiecha

Abstract


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


rfc; ACA; database

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References


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DOI: http://dx.doi.org/10.17951/ai.2016.16.1.40
Data publikacji: 2016-10-04 09:01:49
Data złożenia artykułu: 2016-05-17 09:29:04

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Copyright (c) 2016 Barbara Łukawska, Grzegorz Łukawski, Krzysztof Sapiecha

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