Our researchers, Associate Professor Dijana Oreški, Ph.D., LOUISE/SIMON leader, and Assist. Professor Nikola Kadoić, Ph.D., participated in the MIPRO 2022 conference held on 23.-27. May in Opatija. Together with young researcher and Ph.D. candidate Dunja Višnjić, they prepared the paper “Discretization of numerical meta-features into categorical: analysis of educational and business data sets”. The paper has been successfully presented by Dijana Oreški.
Besides participating in the Mipro conference Digital economics – Digital society in which the paper was evaluated and accepted, our researcher participated in other sessions including the opening ceremony, different invited lectures, and other Mipro conferences that are in the scope of the project and research interests.
Discretization of numerical meta-features into categorical: analysis of educational and business data sets
Abstract. Meta-learning is learning from previous experience gained while applying learning algorithms to different data. Meta-learning consists of three steps: (i) establishing meta-features, (ii) performing learning, and (iii) prediction. This paper focuses on the first step, meta-features. Meta-features are a mix of numerical and categorical variables. We build upon the idea that learning from numerical meta-features is often less effective and less efficient than learning from categorical meta-features. Thus, the objective of this study is to discretize numerical meta-features into categorical values. An overview of meta-features is given in the paper, along with a taxonomy of discretization methods. In addition, a survey of significant discretization methods is provided. Then, discretization is performed on 58 datasets selected from two domains of social sciences: educational and business domains. Research results are discussed, and contributions for meta-learning process improvement are provided.