Knowledge transfer for feature generation in document classification
Document Type
Conference Proceeding
Publication Date
12-1-2009
Abstract
One important problem in machine learning is how to extract knowledge from prior experience, then transfer and apply this knowledge in new learning tasks. To address this problem, transfer learning leverages information from (supervised) learning on related tasks to facilitate the current learning task. Self-taught learning uses information extracted from (unsupervised) learning on related data. In this paper, we propose a new method for knowledge extraction, transfer and application in classification. We consider document classification where we mine correlation relationships among the words from a set of documents and compile a collection of correlation relationships as prior knowledge. This knowledge is then applied to generate new features for classifying documents in classes/types different from the ones which we obtain the correlation relationships from. Our experiment results show that the correlation-based knowledge transfer helps to reduce classification errors. © 2009 IEEE.
Publication Source (Journal or Book title)
8th International Conference on Machine Learning and Applications, ICMLA 2009
First Page
255
Last Page
260
Recommended Citation
Zhang, J., & Shakya, S. (2009). Knowledge transfer for feature generation in document classification. 8th International Conference on Machine Learning and Applications, ICMLA 2009, 255-260. https://doi.org/10.1109/ICMLA.2009.90