Learning spatial decision tree for geographical classification: a summary of results

Authors:
Zhe Jiang;Shashi Shekhar;Pradeep Mohan;Joseph Knight;Jennifer Corcoran
Affiliations:
University of Minnesota;University of Minnesota;University of Minnesota;University of Minnesota;University of Minnesota
Venue:
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Year:
2012

Citing 4
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Induction of Decision Trees

Machine Learning
The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition
Global and local spatial autocorrelation in predictive clustering trees

DS'11 Proceedings of the 14th international conference on Discovery science

Quantified Score

Hi-index	0.00

Visualization

Abstract

Given learning samples from a spatial raster dataset, the geographical classification problem aims to learn a decision tree classifier that minimizes classification errors as well as salt-n-pepper noise. The problem is important in many applications, such as land cover classification in remote sensing and lesion classification in medical diagnosis. However, the problem is challenging due to spatial autocorrelation. Existing decision tree learning algorithms, i.e. ID3, C4.5, CART, produce a lot of salt-n-pepper noise in classification results, due to their assumption that data items are drawn independently from identical distributions. In contrast, we propose a spatial decision tree learning algorithm, which incorporates spatial autocorrelation effect by a new spatial information gain (SIG) measure. The proposed approach is evaluated in a case study on a remote sensing dataset from Chanhassen, MN. Case study results show that the proposed approach outperforms the traditional approach in not only reducing salt-n-pepper noise but also improving classification accuracy.