Constructing Scalable Local Distributed Decision Trees Algorithm for Heterogeneous Data Sources
Distributed Decision Trees (DDT) is one of the most popular classification techniques of predictive modeling. On considering the flow of tera bytes of information available in various formats and sources integrating the same may lead to inaccuracy of the data. This papers proposes a new scalable and robust distributed algorithm for constructing distributed decision trees in peer-to-peer environment for the heterogeneous data sources. Computation and communication cost in the peer-to-peer environment is higher and also on chances of reduced accuracy and response time may be higher. Proposed algorithm scales good and also provides the best prediction model in the well known classification technique of distributed decision trees.