# Concept Drift Adaptation with Incremental-Decremental SVM
## 4 Adaptive-Window Incremental-Decremental SVM (AIDSVM)
Second, after finding out which samples must be removed, one would have to decrease all \(\lambda_{c}\) characteristic values for those vectors in a uniform way, and a similar relation to Equation (13) must be derived. AIDSVM could be modified to support regression problems; the incremental-decremental SVM for regression was previously approached in [41; 42; 43]. A natural direction would also be to extend AIDSVM to multiple classes, where an ensemble of incremental SVMs with adaptive windows could be trained in parallel. Conceptualization, H.G. and R.A.; methodology, H.G. ; software, H.G. ; validation, H.G. and R.A.; formal analysis, H.G. and R.A.; investigation, H.G. ; resources, R.A.; data curation, H.G. ; writing--original draft preparation, H.G. ; writing--review and editing, R.A.; visualization, H.G. ; supervision, R.A. All authors have read and agreed to the published version of the manuscript. This research was funded by Transilvania University of Brasov. Not applicable. The authors declare no conflict of interest. Figure 4: Accuracy comparison between fixed-window C-SVM and adaptive-window AIDSVM, on the SINE1 dataset. The instant accuracy evaluated on the next 100 samples is depicted with blue (C-SVM) and green (AIDSVM). Exponentially weighted accuracy (EWA) is also shown, as well as the mean accuracy. ## References

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