@inproceedings{0400816d2b17488a9098c57ac772623f,
title = "Decision-Making of Homogeneous Multiple Classifiers Based on Attribute Characterisation by Discretisation",
abstract = "The paper presents research focused on the decision-making process of multiple classifiers, conditioned by the characterisation of attributes provided by supervised discretisation. This transformation of the input domain imposes a specific distribution of data and features, exploited by the homogeneous ensembles of estimators based on the informativeness of attribute domains in a dataset. The committees of inducers aggregated decisions through several defined voting scenarios. The procedure was applied to two classifiers that worked on selected publicly available datasets with different properties. Performance was studied with particular attention given to characteristics and irregularities of input domains before and after discretisation, sensitivity of learners to various data forms, and consequences of the employed voting schema.",
keywords = "Aggregating Decisions, Decision-Making, Discretisation, Multiple Classifier, Voting",
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\_17",
language = "English",
isbn = "9783031975660",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "205--220",
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",
}