loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Author: David M. W. Powers

Affiliation: Beijing University of Technology and Flinders University, China

ISBN: 978-989-8565-70-9

ISSN: 2184-2809

Keyword(s): Kappa, Correlation, Informedness, Markedness, ROC, AUC, Boosting, Bagging, Adaboost, Multiboost, Machine Learning, Signal Processing, Classifier Fusion, Language Technology, Concept Learning.

Related Ontology Subjects/Areas/Topics: Hybrid Learning Systems ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Perception and Awareness ; Robotics and Automation ; Sensors Fusion ; Signal Processing, Sensors, Systems Modeling and Control ; Signal Reconstruction ; Vision, Recognition and Reconstruction

Abstract: There has been considerable interest in boosting and bagging, including the combination of the adaptive techniques of AdaBoost with the random selection with replacement techniques of Bagging. At the same time there has been a revisiting of the way we evaluate, with chance-corrected measures like Kappa, Informedness, Correlation or ROC AUC being advocated. This leads to the question of whether learning algorithms can do better by optimizing an appropriate chance corrected measure. Indeed, it is possible for a weak learner to optimize Accuracy to the detriment of the more reaslistic chance-corrected measures, and when this happens the booster can give up too early. This phenomenon is known to occur with conventional Accuracy-based AdaBoost, and the MultiBoost algorithm has been developed to overcome such problems using restart techniques based on bagging. This paper thus complements the theoretical work showing the necessity of using chance-corrected measures for evaluation, with em pirical work showing how use of a chance-corrected measure can improve boosting. We show that the early surrender problem occurs in MultiBoost too, in multiclass situations, so that chance-corrected AdaBook and Multibook can beat standard Multiboost or AdaBoost, and we further identify which chance-corrected measures to use when. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.153.39.7

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
M. W. Powers, D. (2013). Adabook and Multibook - Adaptive Boosting with Chance Correction.In Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-8565-70-9, ISSN 2184-2809, pages 349-359. DOI: 10.5220/0004416303490359

@conference{icinco13,
author={David M. W. Powers.},
title={Adabook and Multibook - Adaptive Boosting with Chance Correction},
booktitle={Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2013},
pages={349-359},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004416303490359},
isbn={978-989-8565-70-9},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Adabook and Multibook - Adaptive Boosting with Chance Correction
SN - 978-989-8565-70-9
AU - M. W. Powers, D.
PY - 2013
SP - 349
EP - 359
DO - 10.5220/0004416303490359

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.