Two algorithms for generating structured and unstructured monotone ordinal data sets

Monotone constraints are very common while dealing with multi-attribute ordinal problems. Grinding wheels hardness selection, timely replacements of costly laser sensors in silicon wafer manufacturing, and the selection of the right personnel for sensitive production facilities, are just a few examples of ordinal problems where monotonicity makes sense. In order to evaluate the performance of various ordinal classifiers one needs both artificially generated as well as real world data sets. Two algorithms are presented for generating monotone ordinal data sets. The first can be used for generating random monotone ordinal data sets without an underlying structure. The second algorithm, which is the main contribution of this paper, describes for the first time how structured monotone data sets can be generated.