Ensemble Cross-Conformal Prediction

D. Beganovic; E. Smirnov; H. Tong; Z. Li; F. Zhu; J. Yu

doi:10.1109/ICDMW.2018.00128

Ensemble Cross-Conformal Prediction

D. Beganovic^*, E. Smirnov, H. Tong, Z. Li, F. Zhu, J. Yu

^*Corresponding author for this work

Robots, Agents, Interaction

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

The cross-conformal prediction is an approach to confidence region prediction. It provides a trade-off between the validity and informational efficiency of the prediction regions from one hand and the computational complexity from another. In this paper we introduce a new cross-conformal approach based on ensembles. The new approach is more computationally efficient and provides gains in the validity and informational efficiency of the prediction regions. Hence, it is a good candidate for big data (analytics) when prediction regions with confidence values are required.

Original language	English
Title of host publication	2018 18TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW)
Publisher	IEEE
Pages	870-877
Number of pages	8
ISBN (Print)	9781538692882
DOIs	https://doi.org/10.1109/ICDMW.2018.00128
Publication status	Published - 2018
Event	18th IEEE International Conference on Data Mining Workshops (ICDMW) - SINGAPORE Duration: 17 Nov 2018 → 20 Nov 2018

Conference

Conference	18th IEEE International Conference on Data Mining Workshops (ICDMW)
Period	17/11/18 → 20/11/18

Keywords

Classification algorithms
Big data applications

Access to Document

10.1109/ICDMW.2018.00128

Cite this

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title = "Ensemble Cross-Conformal Prediction",

abstract = "The cross-conformal prediction is an approach to confidence region prediction. It provides a trade-off between the validity and informational efficiency of the prediction regions from one hand and the computational complexity from another. In this paper we introduce a new cross-conformal approach based on ensembles. The new approach is more computationally efficient and provides gains in the validity and informational efficiency of the prediction regions. Hence, it is a good candidate for big data (analytics) when prediction regions with confidence values are required.",

keywords = "Classification algorithms, Big data applications",

author = "D. Beganovic and E. Smirnov and H. Tong and Z. Li and F. Zhu and J. Yu",

year = "2018",

doi = "10.1109/ICDMW.2018.00128",

language = "English",

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pages = "870--877",

booktitle = "2018 18TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW)",

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note = "18th IEEE International Conference on Data Mining Workshops (ICDMW) ; Conference date: 17-11-2018 Through 20-11-2018",

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TY - GEN

T1 - Ensemble Cross-Conformal Prediction

AU - Beganovic, D.

AU - Smirnov, E.

AU - Tong, H.

AU - Li, Z.

AU - Zhu, F.

AU - Yu, J.

PY - 2018

Y1 - 2018

N2 - The cross-conformal prediction is an approach to confidence region prediction. It provides a trade-off between the validity and informational efficiency of the prediction regions from one hand and the computational complexity from another. In this paper we introduce a new cross-conformal approach based on ensembles. The new approach is more computationally efficient and provides gains in the validity and informational efficiency of the prediction regions. Hence, it is a good candidate for big data (analytics) when prediction regions with confidence values are required.

AB - The cross-conformal prediction is an approach to confidence region prediction. It provides a trade-off between the validity and informational efficiency of the prediction regions from one hand and the computational complexity from another. In this paper we introduce a new cross-conformal approach based on ensembles. The new approach is more computationally efficient and provides gains in the validity and informational efficiency of the prediction regions. Hence, it is a good candidate for big data (analytics) when prediction regions with confidence values are required.

KW - Classification algorithms

KW - Big data applications

U2 - 10.1109/ICDMW.2018.00128

DO - 10.1109/ICDMW.2018.00128

M3 - Conference article in proceeding

SN - 9781538692882

SP - 870

EP - 877

BT - 2018 18TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW)

PB - IEEE

T2 - 18th IEEE International Conference on Data Mining Workshops (ICDMW)

Y2 - 17 November 2018 through 20 November 2018

ER -