Sentence examples for a decision forest from inspiring English sources

The comparative analysis of the proposed classifiers has demonstrated that the best results are achieved by a decision forest made of 30 trees.

We then learn the correlation between the motion of two feet using the probabilistic trajectories in a decision forest classifier.

The algorithm selection is made by a decision forest composed of several trees on the basis of the values of a set of heterogeneous features.

This process was repeated on each pair of { L d, T d } (d = 1, 2,..., m), which consequently resulted in a decision forest with m trees.

In this manuscript, a novel method for identifying potentially poorly classified samples is described that is universal to any Decision Forest method.

A Randomized Decision Forest (RDF) is introduced to achieve robust recognition on decomposed indoor objects with raw point data.

We present a novel classification method, decision forest (DF), for class prediction using omics data.

In this article a novel classification method, Decision Forest (DF), is proposed for developing classification models using omics data.

The models were developed by using a novel QSAR method, Decision Forest, which combines the results of multiple heterogeneous but comparable Decision Tree models to produce a consensus prediction.

Votano et al. [13] reported the application of three QSAR methods using artificial neural networks, k-nearest neighbors, and decision forest, to a data set of 3363 diverse compounds.

In this article, we applied a novel consensus QSAR method, called Decision Forest (DF) (Tong et al. 2003b), to classify chemicals into active and inactive categories of ER binding as a priority-setting tool for EDCs.