Name
Abstract | ||
|---|---|---|
In this paper we propose the Local Credibility Concept (LCC), a novel technique for incremental classifiers. It measures the classification rate of the classifier's local models and ensures that the models do not cross the borders between classes, but allows them to develop freely within the domain of their own class. Thus, we reduce the dependency on the order of training samples, an inherent problem of incremental methods, and make the classifier robust w.r.t. selecting the algorithm's parameters. These only influence the number of models, whereas the performance is controlled by the LCC automatically on a local scale. In contrast to other algorithms, the models of our method are more adaptable as they can also shrink and vanish. This allows classes to move their domains in the data space making the LCC-Classifier also applicable to drifting data concepts. We present experiments to demonstrate these capabilities as well as some benchmark tests that show the algorithm's competitive performance. |
Year | Venue | Keywords |
|---|---|---|
2007 | Artificial Intelligence and Applications | benchmark test,data concept,data space,competitive performance,local credibility,classifier robust w,local credibility criterion,local model,incremental method,incremental classifier,local scale |
Field | DocType | Citations |
Data mining,Data space,Credibility,Local scale,Computer science,Incremental methods,Artificial intelligence,Classifier (linguistics),Classification rate,Machine learning | Conference | 1 |
PageRank | References | Authors |
0.39 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Herward Prehn | 1 | 5 | 0.82 |
| Gerald Sommer | 2 | 269 | 21.93 |