VFDT Algorithm for Decision Tree Generation

The purpose of data classification is to construct a classification model. The decision tree algorithm is a more general data classification function approximation algorithm based on machine learning. The decision tree is directed and acyclic. Iterative Dichotomiser 3(ID3) algorithm invented by Ross Quinlan is used to generate decision tree from a dataset. Considering its limitations layer an optimized algorithm is proposed that can effectively avoid favoring the attribute with a large number of attribute values leading to better tree results. It has its limitations with respect to time and with regards to missing values handling. Proposes to implement and use the very fast decision tree (VFDT) algorithm can effectively perform a test-and-train process with a limited segment of data. In contrast with traditional algorithms, the VFDT does not require that the full dataset be read as part of the learning process thus reducing time. As a preemptive approach to minimizing the impacts of imperfect data streams, a data cache and missing-data-guessing mechanism called the auxiliary reconciliation control (ARC) is proposed to function as a within VFDT. The ARC is designed to resolve the data synchronization problems by ensuring data are pipelined into the VFDT one window at a time. At the same time, it predicts missing values, replaces noises, and handles slight delays and fluctuations in incoming data streams before they even enter the VFDT classifier thus equipped better to handle missing values. A practical implementation of the proposed system validates our claim with regard to the efficiency of the VDFT scheme.