Extensions of Ant-Miner Algorithm to Deal with Class Imbalance Problem

Autor:	Ademir Aparecido Constantino, Murilo Zangari, Wesley Romão
Rok vydání:	2012
Předmět:	Statistical classification Class imbalance media_common.quotation_subject Simplicity Algorithm Class (biology) Mathematics media_common
Zdroj:	Intelligent Data Engineering and Automated Learning-IDEAL 2012 ISBN: 9783642326387 IDEAL
DOI:	10.1007/978-3-642-32639-4_2
Popis:	A database has class imbalance when there are more cases of one class then the others. Classification algorithms are sensitive of this imbalance and tend to valorize the majority classes and ignore the minority classes, which is a problem when the minority classes are the classes of interest. In this paper we propose two extensions of the Ant-Miner algorithm to find better rules to the minority classes. These extensions modify, mainly, how rules are constructed and evaluated. The results show that the proposed algorithms found better rules to the minority classes, considering predictive accuracy and simplicity of the discovered rule list.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::aee325c47f05d55ed60833c947afe14b https://doi.org/10.1007/978-3-642-32639-4_2 Zobrazit plný text záznamu