Advertisement

Improving Active Learning for One-Class Classification Using Dimensionality Reduction

  • Mohsen GhazelEmail author
  • Nathalie Japkowicz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10233)

Abstract

This work aims to improve the performance of active learning techniques for one-class classification (OCC) via dimensionality reduction (DR) and pre-filtering of the unlabelled input data. In practice, the input data of OCC problems is high-dimensional and often contains significant redundancy of negative examples. Thus, DR is typically an important pre-processing step to address the high-dimensionality challenge. However, the redundancy has not been previously addressed. In this work, we propose a framework to exploit the detected DR basis functions of the instance space in order to filter-out most of the redundant data. Instances are removed or maintained using an adaptive thresholding operator depending on their distance to the identified DR basis functions. This reduction in the dimensionality, redundancy and size of the instance space results in significant reduction of the computational complexity of active learning for OCC process. For the preserved instances, their distance to the identified DR basis functions is also used in order to select more efficiently the initial training batch as well as additional instances at each iteration of the active training algorithm. This was done by ensuring that the labelled data always contains nearly uniform representation along the different DR basis functions of the instance space. Experimental results show that applying the DR and pre-filtering steps results in better performance of the active learning for OCC.

Keywords

Active learning One-class learning Dimensionality reduction PCA ICA Supervised learning Anomaly detection 

References

  1. 1.
    Bellinger, C., Sharma, S., Japkowicz, N.: One-class versus binary classification: which and when? In: Proceedings of the 11th International Conference on Machine Learning and Applications (ICMLA), vol. 2, pp. 102–106 (2012)Google Scholar
  2. 2.
    He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)CrossRefGoogle Scholar
  3. 3.
    Barnabe-Lortie, V.: Active learning for one-class classiffication. Master’s thesis, School of Electrical Engineering and Computer Science, Faculty of Engineering, University of Ottawa (2015)Google Scholar
  4. 4.
    Fodor, I.K.: A survey of dimension reduction techniques. Technical report UCRLID-148494. Lawrence Livermore National Laboratory, US Department of Energy (2002)Google Scholar
  5. 5.
    Villalba, S., Cunningham, P.: An evaluation of dimension reduction techniques for one-class classification. J. Artif. Intell. Rev. 27(4), 273–294 (2007)CrossRefGoogle Scholar
  6. 6.
    Bilgic, M.: Combining active learning and dynamic dimensionality reduction. In: SIAM International Conference on Data Mining, pp. 696–707 (2012)Google Scholar
  7. 7.
    Davy, M., Luz, S.: Dimensionality reduction for active learning with nearest neighbour classifier in text categorisation problems. In: International Conference on Machine Learning and Applications, pp. 1–8 (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  2. 2.Department of Computer ScienceAmerican UniversityWashington, DCUSA

Personalised recommendations