Discretizing Numerical Attributes: An Analysis of Human Perceptions
This work addresses the challenge of effective discretization for applications like association rule mining, but it is incremental as it builds on existing methods without introducing a new paradigm.
The paper tackled the problem of discretizing numerical attributes in machine learning by analyzing human perceptions and comparing them with two proposed measures, finding that 68.7% of human responses closely aligned with the proposed measures.
Machine learning (ML) has employed various discretization methods to partition numerical attributes into intervals. However, an effective discretization technique remains elusive in many ML applications, such as 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. This research aims to establish a benchmark approach for numerical attribute partitioning. We conduct an extensive analysis of human perceptions of partitioning a numerical attribute and compare these perceptions with the results obtained from our two proposed measures. We also examine the perceptions of experts in data science, statistics, and engineering by employing numerical data visualization techniques. The analysis of collected responses reveals that $68.7\%$ of human responses approximately closely align with the values generated by our proposed measures. Based on these findings, our proposed measures may be used as one of the methods for discretizing the numerical attributes.