Publikacje

Abstract:

The paper presents an original method of dynamic classification of objects from a new domain which lacks an expert knowledge. The method relies on analysis of attributes of objects being classified 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 classification (rfc), which indicates whether the classification and the model of quality Q are reliable. There is no need to collect data about all objects before the classification starts and possibly the best objects ale selected dynamically (on-the- fly) while data concerning consecutive objects are gathered. The method is implemented as a software tool called Artificial Classification Adviser (ACA). Moreover, the paper presents a case study, where the best candidates for firefighting mobile robot operators are
selected.

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An implementation of articial advisor for dynamic classication of objects