Using Machine Learning to Identify Factors Contributing to Mould in the Celje Ceiling Painting

This paper presents the analysis of data about the damaged paintings on the Celje ceiling in the Celje Regional Museum. Because of old age, and due to microclimate conditions the paintings started to deteriorate. The goal of this analysis is to build predictive models for the damage of the paintings from the existing data on those paintings and score different factors for affecting the deterioration (moulding). The data was available through the Institute for the Protection of Cultural Heritage of Slovenia. It was preprocessed in Python and the data mining task (classification and feature ranking) was carried out in Weka. All models were built using 10-fold cross validation, and were evaluated on their accuracy (CA), per class precision and recall, and area under ROC curve (AUC) scores. The obtained results give some insights as to what may cause the mould and appear to be consistent with the knowledge of the domain experts.