Skip to main content

Agricultural Data Classification Based on Rough Set and Decision Tree Ensemble

Buy Article:

$105.00 plus tax (Refund Policy)

Because of explosive growth of agricultural data available, the classification of agricultural data is becoming one of the most important topics in the field of precision agriculture technologies. Rough set theory is a formal mathematical tool to deal with imprecision, vagueness and uncertainty. Ensemble learning is one of widely used techniques for improving the generalization ability of classification models. In this paper, rough set and decision tree ensemble is combined to construct the hybrid classification approach to increase the performance of the prediction of agricultural data. In the proposed approach, rough set theory is employed as a preprocessor to reduce the redundant attributes with no information loss, then a collection of decision tree classifiers is built from bootstrap samples to construct ensemble. An experimental evaluation of different methods is carried out on several publicly available agricultural datasets. The experimental results indicate that the proposed method achieves significant performance improvement.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics


Document Type: Research Article

Publication date: 2012-01-01

More about this publication?
  • The growing interest and activity in the field of sensor technologies requires a forum for rapid dissemination of important results: Sensor Letters is that forum. Sensor Letters offers scientists, engineers and medical experts timely, peer-reviewed research on sensor science and technology of the highest quality. Sensor Letters publish original rapid communications, full papers and timely state-of-the-art reviews encompassing the fundamental and applied research on sensor science and technology in all fields of science, engineering, and medicine. Highest priority will be given to short communications reporting important new scientific and technological findings.
  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Terms & Conditions
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more