Improved decision tree induction

Abstract

Classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases. We suggest improvements to the existing C4.5 decision tree algorithm. In this paper attribute oriented induction (AOI) and relevance analysis are incorporated with concept hierarchy's knowledge and HeightBalancePriority algorithm for construction of decision tree along with Multi level mining. The assignment of priorities to attributes is done by evaluating information entropy, at different levels of abstraction for building decision tree using HeightBalancePriority algorithm. Modified DMQL queries are used to understand and explore the shortcomings of the decision trees generated by C4.5 classifier for education dataset and the results are compared with the proposed approach.

Node Information Collection Routing Algorithm Using Mobile Agent in WSN Initialization Stage

“…One situation is that all nodes location information in WSN is known, and the appropriate intelligent algorithm is used to complete routing plan. Literature [4][5][6] proposed considering MA routing problem into classical TSP problem and solving it by chaotic simulated annealing algorithm, using of multiple parallel links and Bayesian estimation theory. Such algorithms require a lot of priori information.…”