A Projection Pursuit Forest Algorithm for Supervised Classification
This method addresses classification tasks for users in fields like data science, offering an incremental improvement over existing random forest techniques.
The paper tackles classification problems by introducing a new ensemble method called projection pursuit random forest (PPF), which uses linear combinations of variables to account for correlations and achieves better performance than traditional random forests when class separations involve variable combinations.
This paper presents a new ensemble learning method for classification problems called projection pursuit random forest (PPF). PPF uses the PPtree algorithm introduced in Lee et al. (2013). In PPF, trees are constructed by splitting on linear combinations of randomly chosen variables. Projection pursuit is used to choose a projection of the variables that best separates the classes. Utilizing linear combinations of variables to separate classes takes the correlation between variables into account which allows PPF to outperform a traditional random forest when separations between groups occurs in combinations of variables. The method presented here can be used in multi-class problems and is implemented into an R (R Core Team, 2018) package, PPforest, which is available on CRAN, with development versions at https://github.com/natydasilva/PPforest.