Single Criterion Net Flow Using Concordance and Discordance Relations

This article proposes a flow-based method using the single criterion net flow for pattern classification. Traditional single criterion net flow, generated on the basis of net partial concordance indices, measures the preference intensity for one pattern with respect to all others on this criterion. A concordance index measures the intensity of preference for one pattern over another pattern. Contrary to traditional flow-based methods, such as PROMETHEE, in which only the concordance relation is taken into account, the proposed method proposes a new definition of single criterion net flow using both concordance and discordance relations. This can balance pros and cons for the preference of one pattern over another pattern for a criterion. The final classification decision for a new pattern depends on its net flow. Criteria weights are determined using a genetic-algorithm-based approach. Empirical results involving bankruptcy prediction demonstrate that the proposed method performs well compared to other well-known classification methods.