Mining supervised classification performance studies: A meta-analytic investigation

There have been many comparative studies of classification methods in which real datasets are used as a gauge to assess the relative performance of the methods. Since these comparisons often yield inconclusive or limited results on how methods perform, it is often believed that a broader approach combining these studies would shed some light on this difficult question. This paper describes such an attempt: we have sampled the available literature and created a dataset of 5807 classification results. We show that one of the possible ways to analyze the resulting data is an overall assessment of the classification methods, and we present methods for that particular aim. The merits and demerits of such an approach are discussed, and conclusions are drawn which may assist future research: we argue that the current state of the literature hardly allows large-scale investigations.