Abstract / Description of output
Typical approaches to classification treat class labels as disjoint. For each training example, it is assumed that there is only one class label that correctly describes it, and that all other labels are equally bad. We know however, that good and bad labels are too simplistic in many scenarios, hurting accuracy. In the realm of example dependent cost-sensitive learning, each label is instead a vector representing a data point’s affinity for each of the classes. At test time, our goal is not to minimize the misclassification rate, but to maximize that affinity. We propose a novel example dependent cost-sensitive impurity measure for decision trees. Our experiments show that this new impurity measure improves test performance while still retaining the fast test times of standard classification trees. We compare our approach to classification trees and other cost-sensitive methods on three computer vision problems, tracking, descriptor matching, and optical flow, and show improvements in all three domains.
Original language | English |
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Title of host publication | 2013 IEEE International Conference on Computer Vision |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 193-200 |
Number of pages | 8 |
ISBN (Electronic) | 978-1-4799-2840-8 |
DOIs | |
Publication status | Published - 3 Mar 2014 |
Event | 2013 IEEE International Conference on Computer Vision - Sydney, Australia Duration: 1 Dec 2013 → 8 Dec 2013 http://www.pamitc.org/iccv13/index.php |
Publication series
Name | |
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Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
ISSN (Print) | 1550-5499 |
ISSN (Electronic) | 2380-7504 |
Conference
Conference | 2013 IEEE International Conference on Computer Vision |
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Abbreviated title | ICCV 2013 |
Country/Territory | Australia |
City | Sydney |
Period | 1/12/13 → 8/12/13 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- computer vision
- decision trees
- image classification
- learning (artificial intelligence)
- example dependent cost-sensitive learning
- data point affinity
- example dependent cost-sensitive impurity measure
- standard classification trees
- computer vision problems
- tracking
- descriptor matching
- optical flow
- Impurities
- Vectors
- Training
- Vegetation
- Decision trees
- Tracking
- Standards