A Tighter Error Bound for Decision Tree Learning Using PAC Learnability
Abstract
Error bounds for decision trees are generally based on depth or breadth of the tree. In this paper, we propose a bound for error rate that depends both on the depth and the breadth of a specific decision tree constructed from the training samples. This bound is derived from sample complexity estimate based on PAC learnability. The proposed bound is compared with other traditional error bounds on several machine learning benchmark data sets as well as on an image data set used in Content Based Image Retrieval (CBIR). Experimental results demonstrate that the proposed bound gives tighter estimation of the empirical error.
Cite
Text
Pichuka et al. "A Tighter Error Bound for Decision Tree Learning Using PAC Learnability." International Joint Conference on Artificial Intelligence, 2007.Markdown
[Pichuka et al. "A Tighter Error Bound for Decision Tree Learning Using PAC Learnability." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/pichuka2007ijcai-tighter/)BibTeX
@inproceedings{pichuka2007ijcai-tighter,
title = {{A Tighter Error Bound for Decision Tree Learning Using PAC Learnability}},
author = {Pichuka, Chaithanya and Bapi, Raju S. and Bhagvati, Chakravarthy and Pujari, Arun K. and Deekshatulu, Bulusu Lakshmana},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2007},
pages = {1011-1016},
url = {https://mlanthology.org/ijcai/2007/pichuka2007ijcai-tighter/}
}