What´s Wrong with Hit Ratio?

When reporting classifier accuracy, it´s common to use hit ratio as a primary metric. However, hit ratio has a serious flaw. We examine the issues surrounding this flaw and explore its magnitude through an empirical experiment on three multivalued classification data sets, using two well-known machine learning models. The results demonstrate a real problem that we can´t simply overlook, and we propose an alternative-Cohen´s kappa. Like any other metric, it has its own shortcomings, but we believe it should be mandatory in any scientific report about classifier accuracy