An implementation of articial advisor for dynamic classication of objects

• Autorzy: Łukawska B. , Łukawski G. , Sapiecha K.

Identyfikatory

DOI

10.17951/ai.2016.16.1.40

• Języki publikacji: EN

Abstrakty

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.

Słowa kluczowe

EN

rfc; ACA; database

• Wydawca: Wydawnictwo UMCS
• Czasopismo: Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
• Rocznik: 2016
• Tom: Vol. 16, no. 1
• Strony: 40--49
• Opis fizyczny: Bibliogr. 15 poz., rys., tab.

Twórcy

autor

Łukawska B.

• Department of Computer Science, Kielce University of Technology

autor

Łukawski G.

• Department of Computer Science, Kielce University of Technology

autor

Sapiecha K.

• Department of Computer Science, Kielce University of Technology

Bibliografia

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).