Multi-class classification problems for the k-NN algorithm in the case of missing values

In this contribution methods for improving the quality of multi-class classification by the k nearest neighborhood classifiers in the case of large number of missing values in data sets are considered. Two versions of classifiers are compared. In the first case the aggregation of certainty coefficients of the individual classifiers with the use of the arithmetic mean is applied. In the second case interval modelling and interval-valued aggregation functions are involved. It is proved that the classifier which uses interval methods entails a much slower decrease in classification quality.