A cross-region transfer learning method for classification of community service cases with small datasets
作者：
Highlights：
• We seek to classify community service cases with small datasets in data imbalance situation.
• Existing transfer learning methods have high requirements both for the length and openness of case content.
• We use a cross-regional transfer learning to learn the map from local weak classes to non-local strong classes.
• Classifiers equipped with learned maps may use few samples for training.
• Both the cross-region semantic ambiguity and source domain feature representation are considered for effective classification.
摘要
•We seek to classify community service cases with small datasets in data imbalance situation.•Existing transfer learning methods have high requirements both for the length and openness of case content.•We use a cross-regional transfer learning to learn the map from local weak classes to non-local strong classes.•Classifiers equipped with learned maps may use few samples for training.•Both the cross-region semantic ambiguity and source domain feature representation are considered for effective classification.
论文关键词：Community service,Case classification,Small datasets,Transfer learning,Domain adaptation
论文评审过程：Received 4 June 2019, Revised 12 December 2019, Accepted 13 December 2019, Available online 17 December 2019, Version of Record 7 March 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2019.105390
