Issue No. 05 - May (2014 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.134
Amir Ahmad , Fac. of Comput. & Inf. Technol., King Abdulaziz Univ., Rabigh, Saudi Arabia
Gavin Brown , Sch. of Comput. Sci., Univ. of Manchester, Manchester, UK
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.
pattern classification, computational complexity, decision trees
A. Ahmad and G. Brown, "Random Projection Random Discretization Ensembles—Ensembles of Linear Multivariate Decision Trees," in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 5, pp. 1225-1239, 2014.