Abstract
To partition numerical attributes, machine learning (ML) has used a variety of discretization approaches that partition the numerical attribute into intervals. However, an effective method for discretization is still missing in various ML approaches, e.g., association rule mining. Moreover, the existing discretization techniques do not reflect best the impact of the independent numerical factor on the dependent numerical target factor. The main objective of this research is to develop a benchmark approach for partitioning numerical factors. We present an in-depth analysis of human perceptions of partitioning a numerical factor and compare it with one of our proposed measures. We also examine the perceptions of various experts in data science, statistics and engineering disciplines by using a series of graphs with numerical data. The analysis of the collected responses indicates that \(68.7\%\) of the human responses were approximately close to the values obtained by the proposed method. Based on this analysis, the proposed method may be used as one of the methods for discretizing the numerical attributes.
This work has been partially conducted in the project “ICT programme” which was supported by the European Union through the European Social Fund.
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Kaushik, M., Sharma, R., Vidyarthi, A., Draheim, D. (2022). Discretizing Numerical Attributes: An Analysis of Human Perceptions. In: Chiusano, S., et al. New Trends in Database and Information Systems. ADBIS 2022. Communications in Computer and Information Science, vol 1652. Springer, Cham. https://doi.org/10.1007/978-3-031-15743-1_18
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