@inproceedings{3d91a60f20994cbfa0c968f65ca90ac3,
title = "Preserving Informative Content of Condition Attributes in Data Transformations for CRSA",
abstract = "The research work described in the paper addressed the preparation of the input data for the classical rough set approach, with the aim of preserving the informative content of all condition attributes. Instead of ignoring attributes whose values are assigned to a single interval by a supervised discretisation algorithm, such attributes are subjected to unsupervised discretisation processing. In order to examine the informativeness of attributes undergoing the fusion of discretisation methods, reducts and decision rules were induced as popular forms of knowledge representation, especially in the framework of rough set theory. The results obtained were studied from the point of view of the characteristics of knowledge representations and the performance of rule-based classifiers evaluated with test sets discretised in different ways. The conducted experiments demonstrate the validity of the investigated approach.",
keywords = "CRSA, Decision Reduct, Decision Rule, Decision-Making, Discretisation",
author = "Urszula Sta{\'n}czyk and Beata Zielosko and Grzegorz Baron",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; Workshops on Computational Science, which were co-organized with the 25th International Conference on Computational Science, ICCS 2025 ; Conference date: 07-07-2025 Through 09-07-2025",
year = "2025",
doi = "10.1007/978-3-031-97567-7\_15",
language = "English",
isbn = "9783031975660",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "175--189",
editor = "Maciej Paszynski and Barnard, \{Amanda S.\} and Zhang, \{Yongjie Jessica\}",
booktitle = "Computational Science {\textendash} ICCS 2025 Workshops - 25th International Conference, 2025, Proceedings",
address = "Germany",
}