Learning about the Learning Process
| Authors | |
|---|---|
| Year of publication | 2011 |
| Type | Article in Proceedings |
| Conference | Advances in Intelligent Data Analysis X |
| MU Faculty or unit | |
| Citation | |
| Web | http://dx.doi.org/10.1007/978-3-642-24800-9_17 |
| Doi | http://dx.doi.org/10.1007/978-3-642-24800-9_17 |
| Field | Informatics |
| Keywords | Data streams; concept drift; meta-learning; recurrent concepts |
| Description | This work addresses the problem of mining data stream generated in dynamic environments where the distribution underlying the observations may change over time. We present a system that monitors the evolution of the learning process. The system is able to self-diagnose degradations of this process, using change detection mechanisms, and self-repairs the decision models. The system uses meta-learning techniques that characterize the domain of applicability of previously learned models. The meta-learners can detect reccurrence of contexts using unlabeled examples, and take pro-active actions by activating previously learned models. |
| Related projects: |