Cognitively inspired classification for adapting to data distribution changes

In pattern classification, the test data is expected to lie in the domain covered by the training data. But in practical scenarios, this may not necessarily be true. To improve the adaptability, the classifier should be able to generalize well even when there are changes in the input distribution. This paper proposes a cognitively inspired classification framework based on rules and exemplars. It can generalize well even for samples falling outside the region covered by the training data.