Filter based hybrid decision tree construction model for high dimensional anomaly classification

Abstract

Anomaly discovery from the database is a process of filtering uncertain features, so that it can be used wide variety of applications. Anomaly detection on the complex data must take a long time due to the large number of features. In this proposed work, we extended the anomaly detection accuracy in distributed databases using multi-objective distributed decision tree algorithm. Proposed algorithm uses distributed entropy measure for selecting relevant attributes from the databases. Multi-Objective mechanism provides sensitiveness within the attributes as well as on the decision classes. Multi-Objective process introduces lower and upper bound mechanism for each node in the decision tree construction to preserve the data values in the decision rules. Experimental result performs well against different distributed datasets in terms of time and accuracy.