2007/08/01 by Cesare Alippi, Manuel Roveri · 1 citation
Computer Science · #Neural Networks and Applications #Machine Learning and Data Classification #Data Stream Mining Techniques #Classifier (UML) #Computer science #Supervisor #Artificial intelligence #Machine learning #Concept drift #Random subspace method #Adaptive learning #Data mining
paper · doi:10.1109/ijcnn.2007.4371096
openalex publication_date 2007/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Integrating new information in classification systems during their operational life requires adaptive mechanisms able to identify first the presence of valuable information and update then the knowledge base onto which the classifier is configured. In this paper we provide a design solution for adaptive classifiers operating in stationary environments; information provided (whenever available by a supervisor over time) is used to improve the performance of the classification system hence mimicking the asymptotical behavior suggested by the theory. The adaptive classifier relies on k -NNs, here chosen for their learning-free modality (hence easily supporting a real time adaptation mechanism); a novel method is proposed for matching the optimal k (measuring the complexity of the classifier) with the incremental knowledge acquired over time. A large experimental campaign shows the effectiveness of the proposed approach.