Fuzzy Decision Tree Classification Based On The Peano Count Tree Representation
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2012, Vol 1, Issue 4
Abstract
Image classification is one of the important tasks in remote sensing image interpretation in which the image pixels are classified. Many organizations have large quantities of spatial data collected in various application areas; these data collections are growing rapidly and can therefore be considered as spatial data streams. The classification tree is made by recursive partitioning of feature space and is implemented by a set of rules that determine the path to be followed. The Peano Count Tree is a spatial data organization that provides a lossless compressed representation of a spatial data set and facilitates efficient classification and other data mining techniques. Using P-tree structure, fast calculation of measurements, information gain, can be achieved. Another modification aimed at combining symbolic decision trees with approximate reasoning is offered by fuzzy representation. The intent is to exploit complementary advantages of both: popularity in applications to learning from examples, high knowledge comprehensibility of decision trees and ability to deal with inexact and uncertain information of fuzzy representation. However, these spatial data sets are too large to be classified effectively in a reasonable amount of time using existing methods. The main objective of this project is to generate Fuzzy Decision tree classification based on the Peano Count Tree representation.
Authors and Affiliations
K. Rathna Kumari, P. Venkata Kishore, Dr. M. Seetha
Keywords
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