Table 1
Characteristics of the reviewed adaptation approaches. Notations: X: yes; A: depending on a chosen algorithm; for example, training data can be used instead of assumptions about new context and unlabelled data are used only as negative examples; MK: multiclass classification problems only; U: data are vectors of user preferences; base classifier: either ensemble member or the only context-specific model.
| | Method name | Applicability | Training | Additional data | | Suits dissimilar contexts | Suits large varieties of problems/algorithms | Most lightweight | Requires little assumptions about new contexts | For training of base classifier(s) | For classifier selection/combination | Uses contextual data | Uses raw primary data of other contexts | Uses models for other contexts | Uses unlabelled data for the target context |
| | Model selection | | Contextual weighting | X | X | A | A | X | X | A | | | | | Optimising utility function | X | | | | A | A | A | | | A | | Tuning classifiers for small datasets | X | | | X | X | | | | | A | | Cascaded training | X | X | | X | X | | | | | X | | Learning context-specific relations between classifier outputs | X | MK | X | X | | X | | | | A | | Optimising model parameters with evolutionary algorithms | X | X | | X | X | X | | A | X | | | Optimising model parameters with gradient descent | A | X | | A | X | | | A | X | | | Algorithm-specific methods to shift a decision boundary | A | | | X | X | | | A | X | A | | Adapting only selected parameters | X | X | X | | X | X | | A | X | | | Error weighting | | X | | | X | X | | X | A | | | The use of model parameters as training data | A | | | | X | | | | X | | | Vector modification | X | U | X | X | X | | | X | | A | | Modifying a similarity measure | X | U | A | A | X | | | A | A | A | | Target context-specific combinations of cues, obtained in other contexts | X | U | A | | X | X | A | A | | A |
| | Ensembles | | Factor ensembles | X | | X | X | | X | | | | A | | Diversity-based ensembles | X | X | | X | X | | | | | A | | Optimising a pool of classifiers, trained on data for several contexts | | X | X | X | | X | | | X | | | Ensemble of generalisers | | | X | | | X | X | | X | | | Knowledge transfer ensembles | X | X | X | X | | X | A | X | A | A | | Stacked ensembles | X | X | X | | | X | | | | | | Dynamic selection of base classifiers | X | X | A | X | | X | A | A | | | | Sample-selecting ensembles | X | X | | A | X | | | X | A | A |
| | Context as a feature | | Embedding contextual parameters as additional nodes into graphical models | X | | | | X | | X | X | | A | | Using historical contexts as nodes in graphical models | X | | | | X | | A | X | | A | | Using contextual parameters as input features | X | X | | X | X | X | X | X | | A | | Including contextual similarity into a distance measure | X | | X | | | X | X | X | | A |
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