Incremental Learning for Text Document Classification

This paper presents our research in incremental learning for text document classification. Incremental learning is important in text document classification since many applications have huge amount of training data, and training documents become available through time. We propose an incremental learning framework, ILTC(Incremental Learning of Text Classification) that involves the learning of features of text classes followed by an incremental Perceptron learning process. ILTC has the capabilities of incremental learning of new feature dimensions as well as new document classes. We applied the ILTC to a classification system of diagnostic text documents. The experiment results demonstrate that ILTC was able to incrementally learn new knowledge from newly available training data without either referring to the older training data or forgetting the already learnt knowledge.