Programmer's Active Learning: A Broader Perspective of Choices for Real-World Classification Tasks that Matter (Position Paper)
Keyword(s): dual active learning; machine learning classification; interactive data mining; mixed-initiative user- interfaces; programmer productivity aids
Abstract: This position paper opens the discussion about a future kind of active learning where*rather than just asking a domain expert to assign class labels to items*the system directs a proficient data mining programmer to perform a much wider variety of tasks, e.g. writing code to produce more predictive features for distinguishing confused classes, composing regular expressions to extract key-value features from technical text, writing a classification rule for some tight cluster of cases found by the system, or deciding whether the current classifier is satisficing, in view of its limited rate of improvement. Since data mining programmers are already involved in most efforts to develop classifiers for important real-world tasks, the benefits of channeling their talents to optimize their productivity are intriguing, as well as the potential for reducing the time-to-market for deploying an accurate classifier.
Additional Publication Information: To be published at ALRA 2012: Active Learning in Real-World Applications, ECML/PKDD 2012 Workshop
External Posting Date: August 21, 2012 [Fulltext]. Approved for External Publication
Internal Posting Date: August 21, 2012 [Fulltext]