Document stream classification based on transfer learning using latent topics
Yi Cheng Chen, Masato Shirai, Jianquan Liu, Takao Miura · International Journal of Big Data Intelligence · 2017
In this investigation, we propose a classification framework based on transfer learning using latent intermediate domain for document stream classification. In document stream, word frequency changes dramatically because of transition of themes. To classify document stream, we capture new features and modify the classification criteria during the stream. Transfer learning utilises extracted knowledge from source domain to analyse the target domain. We extract latent topics based on topic model from unlabeled documents. Our approach connects each domain using latent topics to classify documents. And we capture change of features by update of intermediate domain in document stream.