An Improved CART Decision Tree for Datasets with Irrelevant Feature

摘要

Data mining tasks results are usually improved by reducing the dimensionality of data. This improvement however is achieved harder in the case that data size is moderate or huge. Although numerous algorithms for accuracy improvement have been proposed, all assume that inducing a compact and highly generalized model is difficult. In order to address above said issue, we introduce Randomized Gini Index (RGI), a novel heuristic function for dimensionality reduction, particularly applicable in large scale databases. Apart from removing irrelevant attributes, our algorithm is capable of minimizing the level of noise in the data to a greater extend which is a very attractive feature for data mining problems. We extensively evaluate its performance through experiments on both artificial and real world datasets. The outcome of the study shows the suitability and viability of our approach for knowledge discovery in moderate and large datasets.