@inproceedings{84918c4c6c064b14bc742f4acc399cc0,
title = "Attribute Relevance and Discretisation in Knowledge Discovery: A Study in Stylometric Domain",
abstract = "The paper demonstrates the research methodology focused on observations of relations between attribute relevance, displayed by rankings, and discretisation. Instead of transforming all continuous attributes before data exploration, the variables were gradually processed, and the impact of such a change on the performance of a classifier was studied. Considerable experiments carried out on stylometric data illustrate that selective discretisation could be more advantageous to predictive accuracy than some uniform transformation of all features.",
keywords = "Attribute ranking, Discretisation, Stylometry",
author = "Urszula Sta{\'n}czyk and Beata Zielosko and Grzegorz Baron",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 23rd International Conference on Computational Science, ICCS 2023 ; Conference date: 03-07-2023 Through 05-07-2023",
year = "2023",
doi = "10.1007/978-3-031-36021-3\_27",
language = "English",
isbn = "9783031360206",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "273--281",
editor = "Ji{\v r}{\'i} Miky{\v s}ka and \{de Mulatier\}, Cl{\'e}lia and Krzhizhanovskaya, \{Valeria V.\} and Sloot, \{Peter M.A.\} and Maciej Paszynski and Dongarra, \{Jack J.\}",
booktitle = "Computational Science – ICCS 2023 - 23rd International Conference, Proceedings",
address = "Germany",
}