RT Journal Article
T1 An Instance Based Learning Model for Classification in Data Streams with Concept Change
A1 Mena Torres, Dayrelis
A1 Aguilar-Ruiz, Jesús Salvador
A1 Rodríguez- Sarabia, Yanet
K1 Data streams
K1 Classification
K1 Concept change
AB Mining data streams has attracted the attention of the scientific community in recent years with the development of new algorithms for processing and sorting data in this area. Incremental learning techniques have been used extensively in these issues. A major challenge posed by data streams is that their underlying concepts can change over time. This research delves into the study of applying different techniques of classification for data streams, with a proposal based on similarity including a new methodology for detect and treatment of concept change. Previous experimentation are conduced with the model because it have some parameters to be tuned. A comparative statistical analysis are presented, that shows the performance of the proposed algorithm.
PB IEEE
YR 2012
FD 2012-10-27
LK https://hdl.handle.net/10433/26332
UL https://hdl.handle.net/10433/26332
LA en
NO 2012 11th Mexican International Conference on Artificial Intelligence
NO Deporte e Informática
DS RIO
RD May 22, 2026