Evaluation of ordinal attributes at value level

We propose a novel context sensitive algorithm for evaluation of ordinal attributes which exploits the information hidden in ordering of attributes’ and class’ values and provides a separate score for each value of the attribute. Similar to feature selection algorithm ReliefF, the proposed algorithm exploits the contextual information via selection of nearest instances. The ordEval algorithm outputs probabilistic factors corresponding to the effect an increase/decrease of attribute’s value has on the class value. While the ordEval algorithm is general and can be used for analysis of any survey with graded answers, we show its utility on an important marketing problem of customer (dis)satisfaction. We develop a visualization technique and show how we can use it to detect and confirm several findings from marketing theory. Responsible editor: Charu Aggarwal.