Incremental Rule Learning and Border Examples Selection from Numerical Data Streams
Дата
Авторы
Ferrer-Troyano,Francisco
Aguilar-Ruiz,Jesús
Riquelme,José
Journal Title
Journal ISSN
Volume Title
Издатель
Journal of Universal Computer Science
Аннотация
Описание
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.
Ключевые слова
classification , decision rules , incremental learning , concept drift , data streams