Adaptive ensemble learning strategy using an assistant classifier for large-scale imbalanced patent categorization

Authors:
Qi Kong;Hai Zhao;Bao-Liang Lu
Affiliations:
Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering and MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Shangha ...;Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering and MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Shangha ...;Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering and MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Shangha ...
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Year:
2010

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Abstract

Automatic patent classification is of great practical value for saving a lot of resources and manpower. As real patent classification tasks are often very-large scale and serious imbalanced such as patent classification, using traditional pattern classification techniques has shown inefficient and ineffective. In this paper, an adaptive ensemble learning strategy using an assistant classifier is proposed to improve generalization accuracy and the efficiency. The effectiveness of the method is verified on a group of real patent classification tasks which are decomposed in multiple ways by using different algorithms as the assistant classifiers.