Abstract
The issues raised in the paper concern knowledge discovery and representation by decision rules induced while changing the nature of input domain by transformations. These topics are relevant to computational science because they address fundamental tasks of data processing and analysis and contribute to the search for advantageous data representation. With real-world problems described by attributes of varied type, their transformations such as discretisation can have a significant impact on the effectiveness of decision-making. When data exploration involves induction of decision rules, even when an algorithm can operate directly on the continuous domain, such as MODLEM which was used in the research, discretisation can be used to simplify data and to find such transformation conditions that lead to improved performance.
Paper Type
Short Paper
DOI
10.62036/ISD.2026.88
Performance of Classifiers Constructed from Rules Induced from Continuous and Discrete Data
The issues raised in the paper concern knowledge discovery and representation by decision rules induced while changing the nature of input domain by transformations. These topics are relevant to computational science because they address fundamental tasks of data processing and analysis and contribute to the search for advantageous data representation. With real-world problems described by attributes of varied type, their transformations such as discretisation can have a significant impact on the effectiveness of decision-making. When data exploration involves induction of decision rules, even when an algorithm can operate directly on the continuous domain, such as MODLEM which was used in the research, discretisation can be used to simplify data and to find such transformation conditions that lead to improved performance.
Recommended Citation
Stanczyk, U. & Baron, G.(2026). Performance of Classifiers Constructed from Rules Induced from Continuous and Discrete Data. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.88