Name
Abstract | ||
|---|---|---|
The ability to detect concept drift, i.e., a structural change in the acquired datastream, and react accordingly is a major achievement for intelligent sensing units. This ability allows the unit, for actively tuning the application, to maintain high performance, changing online the operational strategy, detecting and isolating possible occurring faults to name a few tasks. In the paper, we consider a just-in-time strategy for adaptation; the sensing unit reacts exactly when needed, i.e., when concept drift is detected. Change detection tests (CDTs), designed to inspect structural changes in industrial and environmental data, are coupled here with adaptive k-nearest neighbor and support vector machine classifiers, and suitably retrained when the change is detected. Computational complexity and memory requirements of the CDT and the classifier, due to precious limited resources in embedded sensing, are taken into account in the application design. We show that a hierarchical CDT coupled with an adaptive resource-aware classifier is a suitable tool for processing and classifying sequential streams of data. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TSMC.2013.2252895 | IEEE T. Systems, Man, and Cybernetics: Systems |
Keywords | Field | DocType |
embedded systems,learning (artificial intelligence),pattern classification,support vector machines | Structured support vector machine,Data mining,Change detection,Random subspace method,Computer science,Artificial intelligence,Classifier (linguistics),k-nearest neighbors algorithm,Pattern recognition,Support vector machine,Concept drift,Machine learning,Computational complexity theory | Journal |
Volume | Issue | ISSN |
44 | 3 | 2168-2216 |
Citations | PageRank | References |
11 | 0.52 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cesare Alippi | 1 | 1040 | 115.84 |
| Derong Liu | 2 | 5457 | 286.88 |
| Dongbin Zhao | 3 | 11 | 0.52 |
| Li Bu | 4 | 17 | 2.34 |