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

GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Volumn , Issue , 2012, Pages 390-393

  Jiang, Zhe ^a   Shekhar, Shashi ^a   Mohan, Pradeep ^a   Knight, Joseph ^a   Corcoran, Jennifer ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

Author keywords

land cover classification; spatial autocorrelation aware decision tree; spatial data mining

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION ERRORS; CLASSIFICATION RESULTS; DATA ITEMS; DATA SETS; DECISION TREE CLASSIFIERS; DECISION TREE LEARNING ALGORITHM; LAND COVER CLASSIFICATION; LEARNING SAMPLES; LESION CLASSIFICATION; SPATIAL AUTOCORRELATIONS; SPATIAL DATA MINING; SPATIAL INFORMATIONS;

AUTOCORRELATION; CLASSIFICATION (OF INFORMATION); DECISION TREES; DIAGNOSIS; LEARNING ALGORITHMS; REMOTE SENSING;

GEOGRAPHIC INFORMATION SYSTEMS;

EID: 84872800517     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2424321.2424372     Document Type: Conference Paper

References (14)

1. L. Anselin. Local indicators of spatial association-lisa. Geographical analysis, 27(2):93-115, 1995.
2. A. Bradley. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern recognition, 30(7):1145-1159, 1997.
3. R. Brooks, D. Wardrop, and C. Cole. Inventorying and monitoring wetland condition and restoration potential on a watershed basis with examples from spring creek watershed, pennsylvania, usa. Environmental Management, 38(4):673-687, 2006.
4. M. Celebi, H. Kingravi, Y. Aslandogan, and W. Stoecker. Detection of blue-white veil areas in dermoscopy images using machine learning techniques. In Proc. of SPIE Vol, volume 6144, pages 61445T-1. Citeseer, 2006.
5. M. A. Friedl and C. E. Brodley. Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61(3):399-409, 1997.
6. G. Hay and G. Castilla. Geographic object-based image analysis (geobia): A new name for a new discipline. Object-based image analysis, pages 75-89, 2008.
7. R. Hearne. Evolving water management institutions in the red river basin. Environmental Management, 40(6):842-852, 2007.
8. X. Li and C. Claramunt. A spatial Entropy-Based decision tree for classification of geographical information. Transactions in GIS, 10(3):451-467, 2006.
9. J. Quinlan. Induction of decision trees. Machine learning, 1(1):81-106, 1986.
10. J. Quinlan. C4. 5: programs for machine learning. Morgan kaufmann, 1993.
11. B. Ripley. Classification and regression trees. R package version, pages 1-0, 2005.
12. O. Schabenberger and C. Gotway. Statistical methods for spatial data analysis, volume 64. CRC Press, 2005.
13. D. Stojanova, M. Ceci, A. Appice, D. Malerba, and S. Džeroski. Global and local spatial autocorrelation in predictive clustering trees. In Discovery Science, pages 307-322. Springer, 2011.
14. B. Walsh. How wetlands worsen climate change. Time, Jan. 2010.