Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 25–28 (2018)
Abstract. An important issue in building predictive models is the ability to quickly assess various aspects of the achievable performance of the model to know what outcome we can expect and how to optimally build the model. As instance selection is one of the preprocessing steps that has to be performed anyway, we can use it to obtain the meta-data descriptors for meta-learning systems. When we only need to estimate the classification accuracy of the model, the compression obtained from instance selection is a good approximator. However, when we need to estimate other performance measures such as the precision and sensitivity then the quality of the estimated performance drops. To overcome this issue we propose a new type of compression measure: the balanced compression which shows high correlation with precision and sensitivity of the final classifiers. We also show that the application of the balanced compression as a meta-learning descriptor allows for precise assessment of the model performance, as proved by the presented experimental evaluation.
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