Paper titled “Discretization of numerical meta-features into categorical: analysis of educational and business data sets” coauthored by Dr. Dijana Oreški, Dunja Višnjić and Dr. Nikola Kadoić will be presented at the MIPRO 2022 – The 45th International Convention on Information, Communication and Electronic Technology that is to be held on the very end of May 2022.
The full paper is to be published in MIPRO 2022 Proceedings and also on this webpage.
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.
Keywords – meta-learning; meta-features; educational data; business data; data mining