Convergence detection criteria for classification based on final error rate

One of the tasks of data mining is classification, which provides a mapping from attributes (observations) to pre-specified classes. Classification models are built by using underlying data. In principle, the models built with more data yield better results (are more accurate). However, the relationship between the available data and the performance is not well understood. How much data to use, or when to stop the learning process, are the key questions. In this paper we give a suggestion as when to stop the learning process.