- author = "Wenlong Fu and Bing Xue and Xiaoying Gao and Mengjie Zhang",
- title = "Genetic Programming based Transfer Learning for Document Classification with Self-taught and Ensemble Learning",
- booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019",
- year = "2019",
- editor = "Carlos A. Coello Coello",
- pages = "2260--2267",
- address = "Wellington, New Zealand",
- month = "10-13 " # jun,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, Document Classification, Transfer Learning",
- isbn13 = "978-1-7281-2152-6",
-
DOI = "
doi:10.1109/CEC.2019.8790318",
- size = "8 pages",
- abstract = "Document classification is a common but challenging task in text mining, since the feature set used is often high-dimensional and sparse. Transfer learning has been applied to improve the classification performance of a (target) domain by transferring knowledge from a previously learnt (source) domain. When there are no labels provided for documents in target domains, it is challenging to effectively transfer knowledge from source domains to target domains. In this paper, we develop a new Genetic Programming (GP) based transfer learning method for document classification, which uses the evolved GP programs from the source domain to learn a set of weak GP classification models on the target domain with unlabelled documents, which is called self-taught learning. These weak classifiers are combined with the GP programs transferred from the source domain to predict the labels of test documents in the target domain. The experimental results show that the GP programs from source domains with their weak classifiers can effectively classify documents in the target domain.",
-
notes = "Also known as \cite{8790318}.
IEEE Catalog Number: CFP19ICE-ART",
