|
ABSTRACT
Title |
: |
A NEW PRUNING APPROACH FOR BETTER AND COMPACT DECISION TREES |
Authors |
: |
Ali Mirza Mahmood, Pavani Kapavarapu, Venu Gopal Kavuluru, Mrithyumjaya Rao Kuppa |
Keywords |
: |
Pre-Pruning, Post-Pruning, EBP, Laplace-
Estimate. |
Issue Date |
: |
November 2010 |
Abstract |
: |
The development of computer technology has enhanced
the people’s ability to produce and collect data. Data mining
techniques can be effectively utilized for analyzing the data to
discover hidden knowledge. One of the well known and efficient
techniques is decision trees, due to easy understanding structural
output. But they may not always be easy to understand due to
very big structural output. To overcome this short coming
pruning can be used as a key procedure .It removes overusing
noisy, conflicting data, so as to have better generalization.
However, In pruning the problem of how to make a trade-off
between classification accuracy and tree size has not been well
solved.
In this paper, firstly we propose a new pruning method
aiming on both classification accuracy and tree size. Based upon
the method, we introduce a simple decision tree pruning
technique, and evaluated the hypothesis – Does our new pruning
method yields Better and Compact decision trees? The
experimental results are verified by using benchmark datasets
from UCI machine learning repository. The results indicate that
our new tree pruning method is a feasible way of pruning
decision trees.
|
Page(s) |
: |
2551-2558 |
ISSN |
: |
0975–3397 |
Source |
: |
Vol. 2, Issue.8 |
|