JUCS - Journal of Universal Computer Science 11(8): 1426-1439, doi: 10.3217/jucs-011-08-1426
Incremental Rule Learning and Border Examples Selection from Numerical Data Streams
expand article infoFrancisco J. Ferrer-Troyano‡, Jesús S. Aguilar-Ruiz§, José C. Riquelme|
‡ Computer Science Department, University of Seville, Spain§ Department of Computer Science, University of Seville, Spain| Computer Science Department, University Pablo de Olavide, Spain

Corresponding author: Francisco Ferrer-Troyano ( ferrer@lsi.us.es )

© Francisco Ferrer-Troyano, Jesús Aguilar-Ruiz, José Riquelme.
This article is freely available under the J.UCS Open Content License.
Citation: Ferrer-Troyano FJ, Aguilar-Ruiz JS, Riquelme JC (2005) Incremental Rule Learning and Border Examples Selection from Numerical Data Streams. JUCS - Journal of Universal Computer Science 11(8): 1426-1439. https://doi.org/10.3217/jucs-011-08-1426
Open Access
Abstract
Mining data streams is a challenging task that requires online systems based on incremental learning approaches. This paper describes a classification system based on decision rules that may store up-to-date border examples to avoid unnecessary revisions when virtual drifts are present in data. Consistent rules classify new test examples by covering and inconsistent rules classify them by distance as the nearest neighbour algorithm. In addition, the system provides an implicit forgetting heuristic so that positive and negative examples are removed from a rule when they are not near one another.
Keywords
classification, decision rules, incremental learning, concept drift, data streams