Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings |
| Subtitle of host publication | 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 442-447 |
| Number of pages | 6 |
| ISBN (electronic) | 9781479966820 |
| Publication status | Published - 19 Feb 2015 |
| Event | 2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015 - Waikoloa, United States Duration: 5 Jan 2015 → 9 Jan 2015 |
Abstract
This paper introduces a novel tree induction algorithm called sequential Random Forest (sRF) to improve the detection accuracy of a standard Random Forest classifier. Observations have shown that the overall performance of a forest is strongly influenced by the number of training samples. The main idea is to sequentially adapt the number of training samples per class so that each tree better complements the existing trees in the whole forest. Further, we propose a weighted majority voting with respect to a class and tree specific error rate for decreasing the influence of poorly performing trees. The sRF algorithm shows competing results in comparison to state-of-the-art approaches using two datasets for object recognition, two standard machine learning datasets and three datasets for human action recognition.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Computer Vision and Pattern Recognition
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Proceedings: 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 442-447 7045919.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Sequential boosting for learning a random forest classifier
AU - Baumann, Florian
AU - Ehlers, Arne
AU - Rosenhahn, Bodo
AU - Liu, Wei
PY - 2015/2/19
Y1 - 2015/2/19
N2 - This paper introduces a novel tree induction algorithm called sequential Random Forest (sRF) to improve the detection accuracy of a standard Random Forest classifier. Observations have shown that the overall performance of a forest is strongly influenced by the number of training samples. The main idea is to sequentially adapt the number of training samples per class so that each tree better complements the existing trees in the whole forest. Further, we propose a weighted majority voting with respect to a class and tree specific error rate for decreasing the influence of poorly performing trees. The sRF algorithm shows competing results in comparison to state-of-the-art approaches using two datasets for object recognition, two standard machine learning datasets and three datasets for human action recognition.
AB - This paper introduces a novel tree induction algorithm called sequential Random Forest (sRF) to improve the detection accuracy of a standard Random Forest classifier. Observations have shown that the overall performance of a forest is strongly influenced by the number of training samples. The main idea is to sequentially adapt the number of training samples per class so that each tree better complements the existing trees in the whole forest. Further, we propose a weighted majority voting with respect to a class and tree specific error rate for decreasing the influence of poorly performing trees. The sRF algorithm shows competing results in comparison to state-of-the-art approaches using two datasets for object recognition, two standard machine learning datasets and three datasets for human action recognition.
UR - http://www.scopus.com/inward/record.url?scp=84925388288&partnerID=8YFLogxK
U2 - 10.1109/wacv.2015.65
DO - 10.1109/wacv.2015.65
M3 - Conference contribution
AN - SCOPUS:84925388288
SP - 442
EP - 447
BT - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015
Y2 - 5 January 2015 through 9 January 2015
ER -