An implementation of articial advisor for dynamic classication of objects
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
Full Text:
PDFReferences
Abdi H., Molin P.: Lilliefors/Van Soest's test of normality, https://www.utdallas.edu/herve/Abdi-Lillie2007-pretty.pdf [accessed Feb-2015]
Cichosz P.: Systemy uczące się, WNT, Warszawa 2000
Gas Hazardous Operations Support Team (GHOST): https://www.qinetiq.com/services-products/survivability/UGV/hazmat-and-re-ghting/Pages/ghost.aspx [accessed Feb-2015]
Hand D., Mannila H., Smyth P.: Eksploracja danych, WNT 2005
Internetowy Podręcznik Statystyki, http://www.statsoft.pl/textbook/stathome.html [accessed Feb-
Łukawska B.: Metodologia dynamicznego wyznaczania modeli nieznanych obiektów na przykładzie szkolenia i selekcjonowania operatorów mobilnego robota, rozprawa doktorska, Kielce 2013
Michalewicz Z.: Algorytmy + struktury danych = programy ewolucyjne, WNT, Warszawa 2003
Pawlak Z.: Rough classication, International Journal of Human-Computer Studies, Volume 51, Issue 2, 1999
Quinlan J. R.: Induction of decision trees, Machine Learning, 1986
Quinlan J. R.: C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA, 1993
Rutkowski L.: Metody i techniki sztucznej inteligencji, Wydawnictwo Naukowe PWN, Warszawa 2006
Statistica, StatSoft Polska, http://www.statsoft.pl [accessed Feb-2015]
SuanShu, Numerical Method Inc.: http://numericalmethod.com/suanshu [accessed Jan-2015]
WEKA, The University of Waikato, http://www.cs.waikato.ac.nz/ml/weka [accessed Jan-2015]
Witten, I. A., Frank, E.: Data Mining, Morgan Kaufmann, 2000
DOI: http://dx.doi.org/10.17951/ai.2016.16.1.40
Date of publication: 2016-10-04 09:01:49
Date of submission: 2016-05-17 09:29:04
Statistics
Indicators
Refbacks
- There are currently no refbacks.
Copyright (c) 2016 Barbara Łukawska, Grzegorz Łukawski, Krzysztof Sapiecha
This work is licensed under a Creative Commons Attribution 4.0 International License.