Spatial Data Analysis Using Various Tree Classifiers Ensembled With AdaBoost Approach

Abstract

The Spatial Data is growing very fast but the available statistical techniques are not sufficient to analyze. The existing Spatial Data Mining Techniques also has certain limitations. The size and complexity of the data sets are posing challenges to the research community. In order to overcome these it is required to do deep study on the suitability of the existing Machine Learning Techniques apart from that check for the suitability of hybrid machine learning techniques. In our paper Classifier Ensembling Technique called AdaBoost Approach was applied on the Spatial Data set for rigorous Analysis. The AdaBoost Technique combines multiple weak classifiers into a single Strong Classifier. It is used in conjunction with many machine learning classifier algorithms in order to boost up their performances. In this connection various Tree Classifier Techniques like J48, Random Forest, BF Tree, F Tree, REP Tree, Random Tree, Simple Cart etc., were considered and applied on the Spatial Data set considered and did the comparative study in terms of various performance metric values both in terms of Numerically and Visually and finally made effective conclusions out of that study. This paper also states that ensemble methods perform in better way than any individual classifier.