Difference based query strategies in active learning

In this paper two active learning methods are proposed in the machine learning literature, both of them based on difference calculation idea. One of the new methods is difference sampling query strategy. This strategy calculates a novel difference list and the elements of this list are then able to influence the uncertainty measure of the appropriate unlabelled instance. By taking the ratio of these measures a new informativeness metric is defined, and the aim of the difference sampling strategy is to minimize this ratio. Besides that, expected difference change query strategy was developed using a new metric, the global difference metric for each step. This strategy combines expected model change and uncertainty sampling strategies by taking the expectation of the difference of uncertainty values. The aim of this combined strategy is to query the instance that will most likely result the greatest change in global difference of the next step. The experimental results on image dataset show that both of the difference based sampling query strategies surpass the competitive methods in literature.

[1]  Marco Hutter,et al.  Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study , 2018, IEEE Transactions on Visualization and Computer Graphics.

[2]  Anant Madabhushi,et al.  A Class Balanced Active Learning Scheme that Accounts for Minority Class Problems : Applications to Histopathology , 2009 .

[3]  Thomas Mensink,et al.  Improving the Fisher Kernel for Large-Scale Image Classification , 2010, ECCV.

[4]  Dávid Papp,et al.  Extended Margin and Soft Balanced Strategies in Active Learning , 2018, ADBIS.

[5]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[6]  Dávid Papp,et al.  Balanced Active Learning Method for Image Classification , 2017, Acta Cybern..

[7]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[8]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[9]  Jan Kautz,et al.  Hierarchical Subquery Evaluation for Active Learning on a Graph , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Murat Akcakaya,et al.  A Probabilistic Active Learning Algorithm Based on Fisher Information Ratio , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[12]  Yi Yang,et al.  Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization , 2015, International Journal of Computer Vision.

[13]  Frédo Durand,et al.  On the Importance of Label Quality for Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[14]  Alan F. Blackwell,et al.  Visual discovery and model-driven explanation of time series patterns , 2016, 2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC).

[15]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[16]  Kristen Grauman,et al.  Large-Scale Live Active Learning: Training Object Detectors with Crawled Data and Crowds , 2011, CVPR 2011.

[17]  Jun Zhou,et al.  Maximizing Expected Model Change for Active Learning in Regression , 2013, 2013 IEEE 13th International Conference on Data Mining.

[18]  Mark Craven,et al.  Multiple-Instance Active Learning , 2007, NIPS.

[19]  Chuang-Hua Chueh,et al.  Cross-Domain Opinion Word Identification with Query-By-Committee Active Learning , 2014, TAAI.

[20]  Jun Li,et al.  Active Learning With Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[22]  Manali Sharma,et al.  Evidence-based uncertainty sampling for active learning , 2016, Data Mining and Knowledge Discovery.

[23]  David Cohn,et al.  Active Learning , 2010, Encyclopedia of Machine Learning.