Impact of subsampling and tree depth on random forests

Random forests are ensemble learning methods introduced by Breiman [Mach. Learn. 45 (2001) 5–32] that operate by averaging several decision trees built on a randomly selected subspace of the data set. Despite their widespread use in practice, the respective roles of the different mechanisms at work in Breiman’s forests are not yet fully understood, neither is the tuning of the corresponding parameters. In this paper, we study the influence of two parameters, namely the subsampling rate and the tree depth, on Breiman’s forests performance. More precisely, we prove that quantile forests (a specific type of random forests) based on subsampling and quantile forests whose tree construction is terminated early have similar performances, as long as their respective parameters (subsampling rate and tree depth) are well chosen. Moreover, experiments show that a proper tuning of these parameters leads in most cases to an improvement of Breiman’s original forests in terms of mean squared error.

People and Mobile Robot Classification Through Spatio-Temporal Analysis of Optical Flow

The goal of this work is to distinguish between humans and robots in a mixed human-robot environment. We analyze the spatio-temporal patterns of optical flow-based features along several frames. We consider the Histogram of Optical Flow (HOF) and the Motion Boundary Histogram (MBH) features, which have shown good results on people detection. The spatio-temporal patterns are composed of groups of feature components that have similar values on previous frames. The groups of features are fed into the FuzzyBoost algorithm, which at each round selects the spatio-temporal pattern (i.e. feature set) having the lowest classification error. The search for patterns is guided by grouping feature dimensions, considering three algorithms: (a) similarity of weights from dimensionality reduction matrices, (b) Boost Feature Subset Selection (BFSS) and (c) Sequential Floating Feature Selection (SFSS), which avoid the brute force approach. The similarity weights are computed by the Multiple Metric Learning for large Margin Nearest Neighbor (MMLMNN), a linear dimensionality algorithm that provides a type of Mahalanobis metric Weinberger and Saul, J. MaCh. Learn. Res.10 (2009) 207–244. The experiments show that FuzzyBoost brings good generalization properties, better than the GentleBoost, the Support Vector Machines (SVM) with linear kernels and SVM with Radial Basis Function (RBF) kernels. The classifier was implemented and tested in a real-time, multi-camera dynamic setting.