We propose random hinge forests, a simple, efficient, and novel variant of decision forests. Importantly, random hinge forests can be readily incorporated as a general component within arbitrary computation graphs that are optimized endto-end with stochastic gradient descent or variants thereof. We derive random hinge forest and ferns, focusing on their sparse and efficient nature, their min-ma...

In this paper we study random forests through their connection with a new framework of adaptive nearest neighbor methods. We first introduce a concept of potential nearest neighbors (k-PNN’s) and show that random forests can be seen as adaptively weighted k-PNN methods. Various aspects of random forests are then studied from this perspective. We investigate the effect of terminal node sizes and...

Random forest is an efficient and accurate classification model, which makes decisions by aggregating a set of trees, either voting or averaging class posterior probability estimates. However, tree outputs may be unreliable in presence scarce data. The imprecise Dirichlet model (IDM) provides workaround, replacing point estimates with interval-valued ones. This paper investigates new aggregatio...

Ensembles of randomized decision trees, usually referred to as random forests, are widely used for classification and regression tasks in machine learning and statistics. Random forests achieve competitive predictive performance and are computationally efficient to train and test, making them excellent candidates for real-world prediction tasks. The most popular random forest variants (such as ...

Random forests are known to be good for data mining of classification tasks, because random forests are robust for datasets having insufficient information possibly with some errors. But applying random forests blindly may not produce good results, and a dataset in the domain of rotogravure printing is one of such datasets. Hence, in this paper, some best classification accuracy based on clever...

Random forests have emerged as one of the most commonly used nonparametric statistical methods in many scientific areas, particularly in analysis of high throughput genomic data. A general practice in using random forests is to generate a sufficiently large number of trees, although it is subjective as to how large is sufficient. Furthermore, random forests are viewed as "black-box" because of ...

Random forests, introduced by Leo Breiman in 2001, are a very effective statistical method. The complex mechanism of the method makes theoretical analysis difficult. Therefore, a simplified version of random forests, called purely random forests, which can be theoretically handled more easily, has been considered. In this paper we introduce a variant of this kind of random forests, that we call...

Random forests are one type of the most effective ensemble learning methods. In spite of their sound empirical performance, the study on their theoretical properties has been left far behind. Recently, several random forests variants with nice theoretical basis have been proposed, but they all suffer from poor empirical performance. In this paper, we propose a Bernoulli random forests model (BR...