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.
Multi-class Nearest Neighbour Classifier for Incomplete Data Handling
NAPOLI, CHRISTIAN;
2015-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
