Semi-Synthetic Data for Enhanced SMS Spam Detection

Autor:	Ala' Eshmawi, Suku Nair
Rok vydání:	2014
Předmět:	ComputingMethodologies_PATTERNRECOGNITION Computer science Oversampling Detection performance Sms spam Data mining computer.software_genre computer Semi synthetic
Zdroj:	MEDES
DOI:	10.1145/2668260.2668307
Popis:	In this paper, we study the effect of using Synthetic Minority Oversampling TEchnique on the detection of SMS spam. The study shows an improved spam detection performance of the classifiers trained on semi-synthetic datasets compared to the performance of the same classifiers trained on the original dataset.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::bae02b2a0231cc642f9bf5e51e0b031c https://doi.org/10.1145/2668260.2668307 Zobrazit plný text záznamu