A Novel Metric for Measuring Data Quality in Classification Applications
  (extended version)
Jouseau Roxane Salva Sébastien Samir Chafik
Data quality is a key element for building and optimizing good learning
models. Despite many attempts to characterize data quality, there is still a
need for rigorous formalization and an efficient measure of the quality from
available observations. Indeed, without a clear understanding of the training
and testing processes, it is hard to evaluate the intrinsic performance of a
model. Besides, tools allowing to measure data quality specific to machine
learning are still lacking. In this paper, we introduce and explain a novel
metric to measure data quality. This metric is based on the correlated
evolution between the classification performance and the deterioration of data.
The proposed method has the major advantage of being model-independent.
Furthermore, we provide an interpretation of each criterion and examples of
assessment levels. We confirm the utility of the proposed metric with intensive
numerical experiments and detail some illustrative cases with controlled and
interpretable qualities.