@inproceedings{a520cd06ed904e398d6f25f69d4b2de6,
title = "Analysis of multiple classifiers performance for discretized data in authorship attribution",
abstract = "In authorship attribution domain single classifiers are often employed in research as elements of decision system. On the other hand, there is intuitive prediction that the use of multiple classifier with fusion of their outcomes may improve the quality of the investigated system. Additionally, discretization can be applied for input data which can be beneficial for the classification accuracy. The paper presents performance analysis of some multiple classifiers basing on the majority voting rule. Ensembles were composed from eight single well known classifiers. Influence of different discretization methods on the quality of the analyzed systems was also investigated.",
keywords = "Authorship attribution, Discretization, Ensemble classifier, Majority voting, Multiple classifier",
author = "Grzegorz Baron",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2018.; 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017 ; Conference date: 21-06-2017 Through 23-06-2017",
year = "2018",
doi = "10.1007/978-3-319-59424-8\_4",
language = "English",
isbn = "9783319594231",
series = "Smart Innovation, Systems and Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "33--42",
editor = "Howlett, \{Robert J.\} and Jain, \{Lakhmi C.\} and Jain, \{Lakhmi C.\} and Ireneusz Czarnowski and Howlett, \{Robert J.\} and Jain, \{Lakhmi C.\}",
booktitle = "Intelligent Decision Technologies 2017 - Proceedings of the 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017",
address = "Germany",
}