Uncertainty Reduction in Collaborative Bootstrapping: Measure and Algorithm

Yunbo Cao, Hang Li, Li Lian

Abstract: This paper proposes the use of uncertainty reduction in machine learning methods such as co-training and bilingual boot-strapping, which are referred to, in a general term, as 'collaborative bootstrapping'. The paper indicates that uncertainty reduction is an important factor for enhancing the performance of collaborative bootstrapping. It proposes a new measure for representing the degree of uncertainty correlation of the two classifiers in collaborative bootstrapping and uses the measure in analysis of collaborative bootstrapping. Furthermore, it proposes a new algorithm of collaborative bootstrapping on the basis of uncertainty reduction. Experimental results have verified the correctness of the analysis and have demonstrated the significance of the new algorithm.