Probabilistic Active Learning: A Short Proposition
Georg Krempl, Daniel Kottke, Myra Spiliopoulou · Frontiers in artificial intelligence and applications · 2014
Active Mining of Big Data requires fast approaches that ideally select for a user-specified performance measure and arbitrary classifier the optimal instance for improving the classification performance. Existing generic approaches are either slow, like error reduction, or heuristics, like uncertainty sampling. We propose a novel, fast yet versatile approach that directly optimises any user-specified performance measure: Probabilistic Active Learning (PAL).