

![0_image_0.png](0_image_0.png)

(when sequential in time instances are selected as a training set). The choice of adaptivity strategy strongly depends on the assumption about the change type, discussed in the previous section. For sudden drift windowing strategies are generally preferred, while for gradual drift and reoccurring contexts instance selection strategies are preferred.

## 2.4 Model Selection

In this thesis we use the generalization error as the primary measure of the concept drift learner performance. Thus for model selection (training) purposes the procedure of estimation the expected generalization error for target instance Xt+1 at every time step needs to be defined. The two main options are:
1. theoretical evaluation of the generalization error, and 2. estimation of the generalization error using cross validation.

In any case error estimation choice is strongly related to the future assumption, because it depends on the expectation regarding the future data source St+1.

The design process of a concept drift learner is graphically illustrated in Figure 5 (a). We see relations (1) and (2) as key issues in designing concept drift learners. (1) the strategies selected to make the learners adaptive would strongly depend on the assumption about the change type, present in the data. (2) the model selection and evaluation strategies would strongly depend on the assumption about the future data source, on which the learner will be applied.