@inproceedings{8aad1e1f18144e17ad103675f1178a11,
title = "Establishing relevance of characteristic features for authorship attribution with ANN",
abstract = "Authorship attribution is perceived as a task of the paramount importance within stylometric analysis of texts. It encompasses author characterisation and comparison, and by observation and recognition of patterns in individual stylistic traits enables confirmation or rejection of authorship claims. Stylometry requires reliable textual descriptors and knowledge about their relevance for the case under study. One of the possible ways to evaluate this relevance is to employ a feature selection and reduction algorithm in the wrapper model. The paper presents research on such procedure applied to artificial neural networks used to categorise literary texts with respect to their authors, with importance of attributes discovered through sequential backward search.",
keywords = "Authorship Attribution, Characteristic Feature, Feature Relevance, Feature Selection, Sequential Backward Search, Stylometry",
author = "Urszula Sta{\'n}czyk",
year = "2013",
doi = "10.1007/978-3-642-40173-2\_1",
language = "English",
isbn = "9783642401725",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 2",
pages = "1--8",
booktitle = "Database and Expert Systems Applications - 24th International Conference, DEXA 2013, Proceedings",
address = "Germany",
edition = "PART 2",
note = "24th International Conference on Database and Expert Systems Applications, DEXA 2013 ; Conference date: 26-08-2013 Through 29-08-2013",
}