DocumentCode :
3424560
Title :
Comparing machine learning classification schemes - a GIS approach
Author :
Lazar, Alina ; Shellito, Bradley A.
Author_Institution :
Dept. of Comput. Sci. & Inf. Syst., Youngstown State Univ., OH, USA
fYear :
2005
fDate :
15-17 Dec. 2005
Abstract :
This project examines the effectiveness of two classification schema: support vector machines (SVM), and artificial neural networks (NN) when applied to geographic (i.e. spatial) data. The context for this study is to examine patterns of urbanization in Mahoning County, OH in relation to several independent driving variables of urban development. These independent variables were constructed using Geographic Information Systems (GIS) and were compared to the dependent variable of the spatial locations of urban areas in Mahoning County. The classification techniques were used in conjunction with the GIS-created variables to predict the location of urban areas within Mahoning County. A comparison of the accuracy of the techniques is presented and conclusions drawn concerning which of the variables are the most influential on urban patterns in the region. Lastly, a spatial analysis of the prediction error is performed for each method.
Keywords :
geographic information systems; geography; learning (artificial intelligence); neural nets; pattern classification; support vector machines; GIS Approach; Geographic Information Systems; Mahoning County; artificial neural networks; geographic data; machine learning classification; spatial analysis; support vector machines; urban development; urban patterns; Cities and towns; Computer science; Geographic Information Systems; Machine learning; Neural networks; Road transportation; Support vector machine classification; Support vector machines; Urban areas; Water resources;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications, 2005. Proceedings. Fourth International Conference on
Print_ISBN :
0-7695-2495-8
Type :
conf
DOI :
10.1109/ICMLA.2005.16
Filename :
1607434
Link To Document :
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