Improved Classification of the High-Resolution Image Data Using Hoeffding Algoritm

Abstract

With the development of the spatial data mining technologies the researcher are grouping towards using the same in various domains. Once such domain is the high resolution images of the urban land. The process includes the collection of segmented image for the various scenes and the classification technique is used to check the probability that segment belongs to the same urban cover along with the class assignment. The classifier previously make use of the random forest tree classification algorithm to develop the network model for semantic web and attribute selection process. However the attribute selection process accuracy can be further improved using the Hoeffding decision tree algorithm where the node split is controlled through the error rate. It’s an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time. The leaf predicting strategy is optimized for the Hoeffding tree through Naive Bayes adaptive process for predicting the land cover with high accuracy rate. The result were simluated using weka as an open source software.