Statistical analysis with missing data
Statistical analysis with missing data
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Relating reinforcement learning performance to classification performance
ICML '05 Proceedings of the 22nd international conference on Machine learning
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Impact of imputation of missing values on classification error for discrete data
Pattern Recognition
Exploring Early Classification Strategies of Streaming Data with Delayed Attributes
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data
The Journal of Machine Learning Research
Reinforcement learning for rule extraction from a labeled dataset
Cognitive Systems Research
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In some applications, data arrive sequentially and they are not available in batch form, what makes difficult the use of traditional classification systems. In addition, some attributes may lack due to some real-world conditions. For this problem, a number of decisions have to be made regarding how to proceed with the incomplete and unlabeled incoming objects, how to guess its missing attributes values, how to classify it, whether to include it in the training set, or when to ask for the class label to an expert. Unfortunately, no decision works well for all data sets. This data dependency motivates our formulation of the problem in terms of elements of reinforcement learning. The application of this learning paradigm for this problem is, to the best of our knowledge, novel. The empirical results are encouraging since the proposed framework behaves better and more generally than many strategies used isolatedly, and makes an efficient use of human effort (requests for the class label to an expert) and computer memory (the increase of size of the training set).