Readiness assessment of a business intelligence system : empirical evidence from African countries

Abstract EN

-Business intelligence warning system is of crucial importance in knowledge management strategies with the increasing competition and environment complexity.

Although a variety of methods have been applied in this field, there are still some problems remained.

The accurate and sensitive prediction in presence of unequal misclassification costs is an important one.

Learning vector quantization (LVQ) is a powerful tool to solve early warning distress problem as a classification task.

In this paper, a cost-sensitive version of LVQ is proposed which incorporates the cost information in the model.

Experiments on a real data sets show the proposed approach is effective to improve the predictive capability in cost-sensitive situation