'competence' of a base learner, expected in the 'nearest future' (future assumption A.1). The weight is usually a function of the historical performance [83, 141, 142, 150, 72, 7, 115, 163, 131]
in the past or estimated performance using selective cross validation [148, 137, 151, 44, 95] or base learner specific performance estimates [81, 79, 157, 127]. The historical evaluation is restricted to sudden and incremental drifts, while cross validation allow taking into account gradual drifts and reoccurring contexts.

In adaptive ensemble learners much attention is drawn to model evaluation and fusion rules (A.4),
while little attention is drawn to the model construction (A.3). Still there is a number of options how to build diverse base classifiers. Usually the implicit aim is to have at least one classifier in the ensemble trained for each distinct concept. This can be achieved using different training set selection strategies.

The straightforward approach is to divide historical data into blocks, which include instances sequential in time. Often these blocks are non overlapping [142, 150, 148, 7, 115, 72, 81, 79, 95],
sometimes overlapping [44]. These techniques are suitable for sudden and to some extent to incremental drifts, they favor reoccurring contexts. Another approach is using different sized training windows [83, 141, 137, 127], which implicitly assume that once off sudden drift has happened. Training windows are overlapping sequential blocks of instances, but all of them have fixed ending 'now'
(time t). The individual models in ensembles can also be constructed using non sequential instance selection [151]. This technique is more suitable to gradual drift, as well as reoccurring contexts.

Another approach to building diverse base classifiers is to use the same training data, but different types of base learners (e.g. SVM, decision tree, Naive Bayes) [163, 131]. All these techniques build individual models from what has already been seen in the past. In principle base classifiers can also be built adding unseen data, for instance noise or unlabeled testing data, which is listed as our future work.

## 3.1.2 Instance Weighting

Instance weighting weighting methods make another group of evolving adaptation techniques. The algorithms can consist of a single learner [85, 163, 117] or an ensemble [56, 14, 30], but the adaptivity here is achieved not by combination rules, but by systematic training set formation. Ideas from boosting [52] are often employed, giving more attention to the instances which were misclassified.

## 3.1.3 Feature Space

There are models, manipulating feature space to achieve adaptivity. [50] uses ideas from transfer learning to achieve adaptivity. New features are added to the training instances, which contain information from the past model performances. [16] augments the feature space by a time stamp. [73, 152] use dynamic feature space over time. In [3] the variables to observe next are adaptively selected.