Active improvement of hierarchical object features under budget constraints

doi:10.1007/s11704-012-2857-5

Front Comput Sci

2012, Vol. 6

Issue (2) : 143-153 https://doi.org/10.1007/s11704-012-2857-5

RESEARCH ARTICLE

Active improvement of hierarchical object features under budget constraints

Nicolas CEBRON(

)

Multimedia Computing Lab, University of Augsburg, 86159 Augsburg, Germany

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Abstract

When we think of an object in a supervised learning setting, we usually perceive it as a collection of fixed attribute values. Although this setting may be suited well for many classification tasks, we propose a new object representation and therewith a new challenge in data mining; an object is no longer described by one set of attributes but is represented in a hierarchy of attribute sets in different levels of quality. Obtaining a more detailed representation of an object comes with a cost. This raises the interesting question of which objects we want to enhance under a given budget and cost model. This new setting is very useful whenever resources like computing power, memory or time are limited. We propose a new active adaptive algorithm (AAA) to improve objects in an iterative fashion. We demonstrate how to create a hierarchical object representation and prove the effectiveness of our new selection algorithm on these datasets.

Keywords object hierarchy machine learning active learning

Corresponding Author(s): CEBRON Nicolas,Email:cebron@informatik.uni-augsburg.de

Issue Date: 01 April 2012

Cite this article:

Nicolas CEBRON. Active improvement of hierarchical object features under budget constraints[J]. Front Comput Sci, 2012, 6(2): 143-153.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-012-2857-5
https://academic.hep.com.cn/fcs/EN/Y2012/V6/I2/143

1	Rueping S, Scheffer T. Proceedings of the ICML 2005 Workshop on Learning with Multiple Views. 2005
2	Adelson E H, Anderson C H, Bergen J R, Burt P J, Ogden J M. Pyramid methods in image processing. RCA Engineer , 1984, 29(6): 33-41
3	Cohn D A, Atlas L, Ladner R E. Improving generalization with active learning. Machine Learning , 1994, 15(2): 201-221 doi: 10.1007/BF00993277
4	Zhou Z H, Li M. Semi-supervised learning by disagreement. Knowledge and Information Systems , 2010, 24(3): 415-439 doi: 10.1007/s10115-009-0209-z
5	MacKay D J C. Information-based objective functions for active data selection. Neural Computation , 1992, 4(4): 590-604 doi: 10.1162/neco.1992.4.4.590
6	Roy N, McCallum A. Toward optimal active learning through sampling estimation of error reduction. In: Proceedings of the 18th International Conference on Machine Learning . 2001, 441-448
7	Cohn D A, Ghahramani Z, Jordan M I. Active learning with statistical models. In: Proceedings of 1994 Neural Information Processing Systems . 1994, 705-712
8	Lindenbaum M, Markovitch S, Rusakov D. Selective sampling for nearest neighbor classifiers. Machine Learning , 2004, 54(2): 125-152 doi: 10.1023/B:MACH.0000011805.60520.fe
9	Freund Y, Seung S H, Shamir E, Tishby N. Selective sampling using the query by committee algorithm. Machine Learning , 1997, 28(2-3): 133-168 doi: 10.1023/A:1007330508534
10	Tong S, Koller D. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research , 2001, 2: 45-66
11	Schohn G, Cohn D. Less is more: active learning with support vector machines. In: Proceedings of the 17th International Conference on Machine Learning . 2000, 839-846
12	Campbell C, Cristianini N, Smola A J. Query learning with large margin classifiers. In: Proceedings of the 17th International Conference on Machine Learning . 2000, 111-118
13	Baram Y, El-Yaniv R, Luz K. Online choice of active learning algorithms. Journal of Machine Learning Research , 2004, 5: 255-291
14	Osugi T, Kun D, Scott S. Balancing exploration and exploitation: a new algorithm for active machine learning. In:Proceedings of the 5th IEEE International Conference on Data Mining . 2005, 330-337 doi: 10.1109/ICDM.2005.33
15	Cebron N, Berthold M R. Active learning for object classification: from exploration to exploitation. Data Mining and Knowledge Discovery , 2009, 18(2): 283-299
16	Balcan M, Beygelzimer A, Langford J. Agnostic active learning. In: Proceedings of the 23rd International Conference on Machine Learning . 2006, 65-72
17	Dasgupta S, Kalai A T, Monteleoni C. Analysis of perceptron-based active learning. Journal ofMachine Learning Research , 2009, 10: 281-299
18	Zhao W, He Q, Ma H, Shi Z. Effective semi-supervised document clustering via active learning with instance-level constraints. Knowledge and Information Systems (in Press)
19	Basu S, Banerjee A, Mooney R J. Active semi-supervision for pairwise constrained clustering. In: Proceedings of the 4th SIAM International Conference on Data Mining . 2004, 333-344
20	Kapoor A, Horvitz E, Basu S. Selective supervision: guiding supervised learning with decision-theoretic active learning. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence . 2007, 877-882
21	Settles B, Craven M, Friedland L. Active learning with real annotation costs. In: Proceedings of the NIPS Workshop on Cost-Sensitive Learning . 2008, 1-10
22	Zheng Z, Padmanabhan B. On active learning for data acquisition. In: Proceedings of 2002 IEEE International Conference on Data Mining . 2002, 562-569
23	Saar-Tsechansky M, Melville P, Provost F. Active feature-value acquisition. Management Science , 2009, 55(4): 664-684
24	Viola P A, Jones M J. Rapid object detection using a boosted cascade of simple features. In: Proceedings of 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . 2001, 511-518
25	Sch?lkopf B, Burges C J C, Smola A J. Advances in Kernel Methods: Support Vector Learning. Cambridge: MIT Press, 1999
26	Abbasnejad M E, Ramachandram D, Mandava R. A survey of the state of the art in learning the kernels. Knowledge and Information Systems (in Press)
27	McCallum A, Nigam K. Employing EMand pool-based active learning for text classification. In: Proceedings of the 15th International Conference on Machine Learning . 1998, 350-358
28	Mandel M I, Poliner G E, Ellis D P W. Support vector machine active learning for music retrieval. Multimedia Systems , 2006, 12(1): 3-13
29	Wang L, Chan K L, Zhang Z. Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval. In: Proceedings of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . 2003, 629-634
30	Luo T, Kramer K, Goldgof D B, Hall L O, Samson S, Remsen A, Hopkins T. Active learning to recognize multiple types of plankton. Journal of Machine Learning Research , 2005, 6: 589-613
31	Warmuth M K, Liao J, R?tsch G, Mathieson M, Putta S, Lemmen C. Active learning with support vector machines in the drug discovery process. Journal of Chemical Information and Computer Sciences , 2003, 43(2): 667-673
32	Cauwenberghs G, Poggio T. Incremental and decremental support vector machine learning. In: Proceedings of 2000 Neural Information Processing Systems . 2000, 409-415
33	van der Heijden F, Duin R, de Ridder D, Tax D M J. Classification, Parameter Estimation and State Estimation: An Engineering Approach Using Matlab. New York: Wiley, 2004
34	Zernike F. Diffraction theory of the cut procedure and its improved form, the phase contrast method. Physica , 1934, 1: 689-704
35	Asuncion A, Newman D J. UCI Machine Learning Repository, 2007

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