[en] In this paper, an on-line interactive method is proposed for learning a linear classifier. This problem is studied within the Active Learning (AL) framework where the learning algorithm sequentially chooses unlabelled training samples and requests their class labels from an oracle in order to learn the classifier with the least queries to the oracle possible. Additionally' a constraint is introduced into this interactive learning process which limits the percentage of the samples from one “unwanted” class under a certain threshold. An optimal AL solution is derived and implemented with a sophisticated, accurate and fast Bayesian Learning method, the Expectation Propagation (EP) and its performance is demonstrated through numerical simulations.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
TSAKMALIS, Anestis ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
CHATZINOTAS, Symeon ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
OTTERSTEN, Björn ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Constrained bayesian active learning of linear classifier
Date de publication/diffusion :
2018
Nom de la manifestation :
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Lieu de la manifestation :
Calgary, Canada
Date de la manifestation :
15-04-2018 to 20-04-2018
Manifestation à portée :
International
Titre de l'ouvrage principal :
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Maison d'édition :
IEEE
ISBN/EAN :
978-1-5386-4658-8
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
Projet européen :
H2020 - 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems
D. A. Cohn, A. Ghahramani, and M. I. Jordan, "Active Learning with Statistical Models, " J. Artif. Intell. Res., vol. 4, pp. 129-145, 1996.
B. Gopalakrishnan and N. D. Sidiropoulos, "Cognitive Transmit Beamforming from Binary CSIT, " IEEE Trans. Wireless Commun., vol. 14, no. 2, pp. 895-906, Feb. 2014.
A. Tsakmalis, S. Chatzinotas, and B. Ottersten, "Centralized Power Control in Cognitive Radio Networks Using Modulation and Coding Classification Feedback, " IEEE Trans. Cognitive Commun. And Networking, vol. 2, no. 3, pp. 223-237, Sept. 2016.
U. Louche and L. Ralaivola, "From Cutting Planes Algorithms to Compression Schemes and Active Learning, " in Proc. International Joint Conf. on Neural Networks (IJCNN), Jul. 2015.
K. Chaloner and I. Verdinelli, "Bayesian Experimental Design: A Review, " Stat. Sci., vol. 10, no. 3, pp. 273-304, Aug. 1995.
I. Ben-Gal and M. Caramanis, "Sequential DOE via dynamic programming, " IIE Trans., vol. 34, no. 12, pp. 1087-1100, May 2002.
D. S. Zois, M. Levorato, and U. Mitra, "Active Classification for POMDPs: A Kalman-Like State Estimator, " IEEE Trans. Signal Process., vol. 62, no. 23, pp. 6209-6214, Dec. 2014.
A. Tsakmalis, S. Chatzinotas, and B. Ottersten, "Interference Constraint Active Learning with Uncertain Feedback for Cognitive Radio Networks, " IEEE Trans. Wireless Commun., vol. 16, no. 7, pp. 4654-4668, Jul. 2017.
M. Ono, M. Pavone, Y. Kuwata, and J. Balaram, "Chance-Constrained Dynamic Programming with Application to Risk-Aware Robotic Space Exploration, " Springer US Autonomous Robots, vol. 39, no. 4, pp. 555-571, Dec. 2015.
L. M. Lopez-Ramos, A. G. Marques, and J. Ramos, "Jointly Optimal Sensing and Resource Allocation for Multiuser Interweave Cognitive Radios, " IEEE Trans. Wireless Commun., vol. 13, no. 11, pp. 5954-5967, Nov. 2014.
D. S. Zois and U. Mitra, "Active State Tracking With Sensing Costs: Analysis of Two-States and Methods for n-States, " IEEE Trans. Signal Process., vol. 65, no. 11, pp. 2828-2843, Jun. 2017.
J. L. Williams, J. W. Fisher, and A. S. Willsky, "Approximate Dynamic Programming for Communication-Constrained Sensor Network Management, " IEEE Trans. Signal Process., vol. 55, no. 8, pp. 4300-4311, Aug. 2007.
T. Minka, A family of algorithms for approximate Bayesian inference, Ph. D. thesis, MIT, Cambridge, MA, 2001.
R. Kan and C. Robotti, "On Moments of Folded and Truncated Multivariate Normal Distributions, " Journal of Computational and Graphical Statistics, Apr. 2017.
V. N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, 2000.
K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, Cambridge, MA, USA, 2012.
A. Tsakmalis, S. Chatzinotas, and B. Ottersten, "Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks, " https://arxiv. org/abs/1710. 08335, 2017.
D. P. Bertsekas, Dynamic Programming and Optimal Control, Athena Scientific, Belmont, MA, 1995.
