2018/06/18 by Alejandro Cervantes, Cervantes, Alejandro, Christian Gagné +5
Computer Science · Mathematics · #Data Stream Mining Techniques #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1806.06610
arxiv created 2018/06/18 · openalex publication_date 2018/06/18 · arxiv updated 2018/06/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Incremental learning from non-stationary data poses special challenges to the field of machine learning. Although new algorithms have been developed for this, assessment of results and comparison of behaviors are still open problems, mainly because evaluation metrics, adapted from more traditional tasks, can be ineffective in this context. Overall, there is a lack of common testing practices. This paper thus presents a testbed for incremental non-stationary learning algorithms, based on specially designed synthetic datasets. Also, test results are reported for some well-known algorithms to show that the proposed methodology is effective at characterizing their strengths and weaknesses. It is expected that this methodology will provide a common basis for evaluating future contributions in the field.