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Polyhedral classifier for target detection: a case study: colorectal cancer

Authors:

Vikas C. RaykarAuthors Info & Claims

ICML '08: Proceedings of the 25th international conference on Machine learning

Pages 288 - 295

https://doi.org/10.1145/1390156.1390193

Published: 05 July 2008 Publication History

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Abstract

In this study we introduce a novel algorithm for learning a polyhedron to describe the target class. The proposed approach takes advantage of the limited subclass information made available for the negative samples and jointly optimizes multiple hyperplane classifiers each of which is designed to classify positive samples from a subclass of the negative samples. The flat faces of the polyhedron provides robustness whereas multiple faces contributes to the flexibility required to deal with complex datasets. Apart from improving the prediction accuracy of the system, the proposed polyhedral classifier also provides run-time speedups as a by-product when executed in a cascaded framework in real-time. We evaluate the performance of the proposed technique on a real-world Colon dataset both in terms of prediction accuracy and online execution speed.

References

[1]

Bezdek, J., & Hathaway, R. (2003). Convergence of alternating optimization. Neural, Parallel Sci. Comput., 11, 351--368.

Digital Library

Google Scholar

[2]

Bi, J., Periaswamy, S., Okada, K., Kubota, T., Fung, G., Salganicoff, M., & Rao, R. B. (2006). Computer aided detection via asymmetric cascade of sparse hyperplane classifiers. Proceedings of the Twelfth Annual SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 837--844). Philadelphia, PA.

Digital Library

Google Scholar

[3]

Brew, A., Grimaldi, M., & Cunningham, P. (2007). An evaluation of one-class classification techniques for speaker verification (Technical Report UCD-CSI-2007-8). University College Dublin.

Digital Library

Google Scholar

[4]

Chen, Y., Zhou, X. S., & Huang, T. S. (2001). One-class svm for learning in image retrieval. ICIP (1) (pp. 34--37).

Google Scholar

[5]

Dundar, M., & Bi, J. (2007). Joint optimization of cascaded classifiers for computer aided detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1--8).

Crossref

Google Scholar

[6]

Jemal, D., Tiwari, R., Murray, T., Ghafoor, A., Saumuels, A., Ward, E., Feuer, E., & Thun, M. (2004). Cancer statistics.

Google Scholar

[7]

Manevitz, L. M., & Yousef, M. (2001). One-class svms for document classification. Journal of Machine Learning Research, 2, 139--154.

Digital Library

Google Scholar

[8]

Mika, S., Rätsch, G., & Müüller, K.-R. (2000). A mathematical programming approach to the kernel fisher algorithm. NIPS (pp. 591--597).

Google Scholar

[9]

Murth, S. K., Kasif, S., & Salzberg, S. (1994). A system for induction of obligue decision trees. Journal of Artificial Intelligence Research, 2, 1--33.

Crossref

Google Scholar

[10]

Scholkopf, B., Platt, O., Shawe-Taylor, J., Smola, A., & Williamson, R. (1999). Estimating the support of a high-dimensional distribution.

Google Scholar

[11]

Tax, D. M. J., & Duin, R. P. W. (1999). Support vector domain description. Pattern Recognition Letters, 20, 1191--1199.

Digital Library

Google Scholar

[12]

Tipping, M. E. (2000). The relevance vector machine. In S. Solla, T. Leen and K.-R. Muller (Eds.), Advances in neural information processing systems 12, 652--658. Cambridge, MA: MIT Press.

Google Scholar

[13]

Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.

Digital Library

Google Scholar

[14]

Viola, P., & Jones., M. (2004). Robust real-time face detection. International Journal of Computer Vision, 57.

Digital Library

Google Scholar

Cited By

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Averkov GHojny CSchymura M(2023)Efficient MIP techniques for computing the relaxation complexityMathematical Programming Computation10.1007/s12532-023-00241-915:3(549-580)Online publication date: 11-Apr-2023
https://doi.org/10.1007/s12532-023-00241-9
Zamaraeva EŽunić J(2022)A characterization of 2-threshold functions via pairs of prime segmentsTheoretical Computer Science10.1016/j.tcs.2022.03.025Online publication date: Mar-2022
https://doi.org/10.1016/j.tcs.2022.03.025
SAĞLAMLAR H(2021)Karmaşık verileri sınıflandırabilen çok merkezli çok yüzlü konik sınıflandırıcılarGazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi10.17341/gazimmfd.79955636:4(1817-1830)Online publication date: 2-Sep-2021
https://doi.org/10.17341/gazimmfd.799556
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Index Terms

Polyhedral classifier for target detection: a case study: colorectal cancer
1. Computing methodologies
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Computational geometry

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Information

Published In

ICML '08: Proceedings of the 25th international conference on Machine learning

July 2008

1310 pages

ISBN:9781605582054

DOI:10.1145/1390156

General Chair:
William Cohen
Carnegie Mellon University
,
Program Chairs:
Andrew McCallum
University of Massachusetts Amherst
,
Sam Roweis
University of Toronto and Google

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 July 2008

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ICML '08

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Microsoft Research
Intel
IBM

ICML '08: The 25th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

July 5 - 9, 2008

Helsinki, Finland

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Overall Acceptance Rate 140 of 548 submissions, 26%

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Cited By

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https://doi.org/10.1007/s12532-023-00241-9
Zamaraeva EŽunić J(2022)A characterization of 2-threshold functions via pairs of prime segmentsTheoretical Computer Science10.1016/j.tcs.2022.03.025Online publication date: Mar-2022
https://doi.org/10.1016/j.tcs.2022.03.025
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https://doi.org/10.17341/gazimmfd.799556
Cevikalp HSaglamlar H(2021)Polyhedral Conic Classifiers for Computer Vision Applications and Open Set RecognitionIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2019.293445543:2(608-622)Online publication date: 1-Feb-2021
https://doi.org/10.1109/TPAMI.2019.2934455
Jain B(2021)Warped softmax regression for time series classificationKnowledge and Information Systems10.1007/s10115-020-01533-5Online publication date: 2-Jan-2021
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Cevikalp HTriggs B(2017)Polyhedral Conic Classifiers for Visual Object Detection and Classification2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR.2017.438(4114-4122)Online publication date: Jul-2017
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