Asking generalized queries to ambiguous oracle
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Ask me better questions: active learning queries based on rule induction
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Do they belong to the same class: active learning by querying pairwise label homogeneity
Proceedings of the 20th ACM international conference on Information and knowledge management
A combined mining-based framework for predicting telecommunications customer payment behaviors
Expert Systems with Applications: An International Journal
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With the assistance of a domain expert, active learning can often select or construct fewer examples to request their labels to build an accurate classifier. However, previous works of active learning can only generate and ask specific queries. In real-world applications, the domain experts (or oracles) are often more readily to answer “generalized queries” with don't-care attributes. The power of such generalized queries is that one generalized query is often equivalent to many specific ones. However, overly general queries are not good as answers from the domain experts (or oracles) can be highly uncertain, and this makes learning difficult. In this paper, we propose a novel active learning algorithm that asks good generalized queries. We, then, extend our algorithm to construct new, hierarchical features for both nominal and numeric attributes. We demonstrate experimentally that our new method asks significantly fewer queries compared with the previous works of active learning, even when the initial labeled data set is very small, and the oracle is inaccurate in class probability estimations. Our method can be readily deployed in real-world data mining tasks where obtaining labeled examples is costly.