The random forest method is a commonly used tool for classification with high-dimensional data that is able to rank candidate predictors through its inbuilt variable importance measures (VIMs). It can be applied to various kinds of regression problems including nominal, metric and survival response variables. While classification and regression problems using random forest methodology have been...

Random forest based Hough-voting techniques have been widely used in a variety of computer vision problems. As an ensemble learning method, the voting weights of leaf nodes in random forest play critical role to generate reliable estimation result. We propose to improve Hough-voting with random forest via simultaneously optimizing the weights of leaf votes and pruning unreliable leaf nodes in t...

Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such a...

We consider the multi-label classification problem in this paper. We propose a randomized ensemble learning algorithm, random tag forest, which is an ensemble of random tag trees. Each tree is built by randomly defining a hierarchical tree structure over a subset of tag vocabulary. Each node in the tree corresponds to a tag in the vocabulary. During testing, a testing example will pass through ...

Classification is the process of assigning a class label to an observation based on its proprieties or attributes. A classification algorithm is applied to a data set, producing a model. By studying the model, insights about the data set structure can be gained. The benefits that a model can bring depend on the model. In this work, a Random Forest model is used for the analysis of data. A Rando...