Missing Values in Monotone Data Sets

This paper explores the problem of missing values in the context of monotone classification. A simple preprocessing method is proposed as an extension of three general approaches for filling in the unknown values (k-nearest neighbour, most frequent value and data point multiplication) so that the monotonicity property of the resulting data set is preserved. The results of the first experiments with the algorithms are reported in order to give more insight in how the method works in practice