COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Knowledge Acquisition form Examples Vis Multiple Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
NeC4.5: Neural Ensemble Based C4.5
IEEE Transactions on Knowledge and Data Engineering
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Decision trees have been widely used in many data mining applications due to their interpretable representation. However, learning an accurate decision tree model often requires a large amount of labeled training data. Labeling data is costly and time consuming. In this paper, we study learning decision trees with lesser labeling cost from two perspectives: data quality and data quantity. At each step of active learning process we learn a random forest and then use it to label a large quantity of unlabeled data. To overcome the large tree size caused by the machine labeling, we generate weighted (soft) labeled data using the prediction confidence of the labeling classifier. Empirical studies show that our method can significantly improve active learning in terms of labeling cost for decision tree learning, and the improvement does not sacrifice the size of decision trees.