@inproceedings{03ece25cecc649e4bde4bf275f217eb6,
title = "Evaluation of rule-based learning and feature selection approaches for classification",
abstract = "Feature selection is typically employed before or in conjunction with classification algorithms to reduce the feature dimensionality and improve the classification performance, as well as reduce processing time. While particular approaches have been developed for feature selection, such as filter and wrapper approaches, some algorithms perform feature selection through their learning strategy. In this paper, we are investigating the effect of the implicit feature selection of the PRISM algorithm, which is rule-based, when compared with the wrapper feature selection approach employing four popular algorithms: decision trees, na{\"i}ve bayes, k-nearest neighbors and support vector machine. Moreover, we investigate the performance of the algorithms on target classes, i.e. where the aim is to identify one or more phenomena and distinguish them from their absence (i.e. non-target classes), such as when identifying benign and malign cancer (two target classes) vs. non-cancer (the non-target class).",
keywords = "feature selection, rule-based learning, wrapper approach",
author = "Fatima Chiroma and Mihaela Cocea and Han Liu",
year = "2019",
month = jan,
day = "1",
doi = "10.4230/OASIcs.ICCSW.2018.6",
language = "English",
volume = "66",
series = "OpenAccess Series in Informatics",
publisher = "Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing",
pages = "1--6",
editor = "Eva Graversen and Edoardo Pirovano",
booktitle = "2018 Imperial College Computing Student Workshop, ICCSW 2018",
address = "Germany",
edition = "Edoardo Pirovano and Eva Graversen",
note = "7th Imperial College Computing Student Workshop, ICCSW 2018 ; Conference date: 20-09-2018 Through 21-09-2018",
}