Active learning query selection with historical information - Davy, Michael (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2009)
This work describes novel methods and techniques to decrease the cost of employing active learning in text categorisation problems. The cost of performing active learning is a combination of labelling effort and computational overhead. Reducing the cost of active learning allows for accurate classifiers to be constructed inexpensively, increasing the number of realworld problems where machine learning solutions can be successfully applied. In this thesis we investigate strategies and techniques to reduce both computational expense and labelling effort in active learning. Critical to the success of active learning is the query selection strategy, which is responsible for identifying informative unlabelled examples. Selecting only the most informative examples will reduce labelling effort as redundant and uninformative examples are ignored. The majority of query selection strategies select queries based on the labelling predictions of the current classifier. This thesis suggests that information from prior iterations of active learning can help select more informative queries in the current iteration. We propose History-based query selection strategies, which incorporate predictions from prior iterations of active learning into the selection of the current query. These strategies have been shown to increase the accuracy of classifiers produced using active learning, thereby reducing labelling effort. In addition, History-based query selection strategies are very efficient since information is reused from previous iterations of active learning. Another contributing factor to the cost of active learning is computational expense. Query selection strategies can require considerable computation to identify the most informative examples. We investigate pre-filtering optimisation for the computationally inefficient error reduction sampling (ERS) query selection strategy. Pre-filtering restricts the number of unlabelled examples considered to a small subset of the pool, constructed using query selection strategy. Optimising ERS using pre-filtering was found to simultaneously reduce computational overhead and the labelling effort.
Keywords: Computer Science, Ph.D.; Ph.D. Trinity College Dublin