Sensitivity and Specificity for Mining Data with Increased Incompleteness

Autor:	Jerzy W. Grzymala-Busse, Shantan R. Marepally
Rok vydání:	2010
Předmět:	Random replacement Computer science Statistical significance Statistics Total error rate Sensitivity (control systems) Data mining computer.software_genre computer Cross-validation
Zdroj:	Artificial Intelligence and Soft Computing ISBN: 9783642132070 ICAISC (1)
DOI:	10.1007/978-3-642-13208-7_45
Popis:	This paper presents results of experiments on data sets that were subjected to increasing incompleteness by random replacement of attribute values by symbols of missing attribute values. During these experiments the total error rate and error rates for all concepts, results of repeated 30 times ten-fold cross validation, were recorded. We observed that for some data sets increased incompleteness might result in a significant improvement for the total error rate and sensitivity (with the significance level of 5%, two-tailed test). These results may be applied for improving data mining techniques, especially for domains in which sensitivity is important, e.g., in medical area.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9a203bd1fc55ff96248c90c8b0063ae5 https://doi.org/10.1007/978-3-642-13208-7_45 Zobrazit plný text záznamu