@inproceedings{5074bd03bfb24cc783184ae8941bcea8,
title = "Multi-class nearest neighbour classifier for incomplete data handling",
abstract = "The basic nearest neighbour algorithm has been designed to work with complete data vectors. Moreover, it is assumed that each reference sample as well as classified sample belong to one and the only one class. In the paper this restriction has been dismissed. Through incorporation of certain elements of rough set and fuzzy set theories into k-nn classifier we obtain a sample based classifier with new features. In processing incomplete data, the proposed classifier gives answer in the form of rough set, i.e. indicated lower or upper approximation of one or more classes. The basic nearest neighbour algorithm has been designed to work with complete data vectors and assumed that each reference sample as well as classified sample belongs to one and the only one class. Indication of more than one class is a result of incomplete data processing as well as final reduction operation.",
keywords = "Missing values, Nearest neighbour, Rough sets",
author = "Nowak, \{Bartosz A.\} and Nowicki, \{Robert K.\} and Marcin Wo{\'z}niak and Christian Napoli",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2015 ; Conference date: 14-06-2015 Through 18-06-2015",
year = "2015",
doi = "10.1007/978-3-319-19324-3\_42",
language = "English",
series = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",
publisher = "Springer Verlag",
pages = "469--480",
editor = "Zurada, \{Jacek M.\} and Zadeh, \{Lotfi A.\} and Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer and Ryszard Tadeusiewicz",
booktitle = "Artificial Intelligence and Soft Computing - 14th International Conference, ICAISC 2015, Proceedings",
address = "Germany",
}