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RANDOM PROJECTION RANDOM DISCRETIZATION ENSEMBLES—ENSEMBLES
OF LINEAR MULTIVARIATE DECISION TREES
ABSTRACT:
In this paper, we present a novel ensemble method random projection random discretization
ensembles (RPRDE) to create ensembles of linear multivariate decision trees by using a
univariate decision tree algorithm. The present method combines the better computational
complexity of a univariate decision tree algorithm with the better representational power of
linear multivariate decision trees. We develop random discretization (RD) method that creates
random discretized features from continuous features. Random projection (RP) is used to create
new features that are linear combinations of original features. A new dataset is created by
augmenting discretized features (created by using RD) with features created by using RP. Each
decision tree of a RPRD ensemble is trained on one dataset from the pool of these datasets by
using a univariate decision tree algorithm. As these multivariate decision trees (because of
features created by RP) have more representational power than univariate decision trees, we
expect accurate decision trees in the ensemble. Diverse training datasets ensure diverse decision
trees in the ensemble. We study the performance of RPRDE against other popular ensemble
techniques using C4.5 tree as the base classifier. RPRDE matches or outperforms other popular
ensemble methods. Experiments results also suggest that the proposed method is quite robust to
the class noise.