Abstract :
In some dynamic environments, the degree of importance of features for classification varies over time. For example, if we want to identify the kinds of birds in a forest, different groups of birds might sing in different time periods. Then we have to change the features to identify the kinds of a bird, e.g., frequency, depending on time of observation. This study deals with such a sequence of feature subsets changing their importance over time. We assume that such a change happens gradually, that is, the case of population drift. To track drifting distributions, we use volume prototypes with a forgetting factor and on the basis of volume prototypes at each time period we extract feature subsets useful for that time.
