It can be seen as a period of time (or a number of instances) after which a classifier is capable of returning a stable performance, i.e., capturing the properties of a new concept. This is especially important in case of a sudden change where base classifiers need to be trained from ...
Figure 4. Process of expand-and-contract instance-based learning (EAC-IBL) involving an evolutionary feature selector. As a methodology, EEAC-IBL could be applied to other pattern recognition domains where the data are fuzzy and ambiguous in nature. The workflow that is depicted by Figure 4 wo...