Mapping complex traits using Random Forests

Object-oriented mapping of landslides using Random Forests

Random forests algorithm in podiform chromite prospectivity mapping in Dolatabad area, SE Iran

The Dolatabad area located in SE Iran is a well-endowed terrain owning several chromite mineralized zones. These chromite ore bodies are all hosted in a colored mélange complex zone comprising harzburgite, dunite, and pyroxenite. These deposits are irregular in shape, and are distributed as small lenses along colored mélange zones. The area has a great potential for discovering further chromite...

CARAF: Complex Aggregates within Random Forests

This paper presents an approach integrating complex aggregate features into a relational random forest learner to address relational data mining tasks. CARAF, for Complex Aggregates within RAndom Forests, has two goals. Firstly, it aims at avoiding exhaustive exploration of the large feature space induced by the use of complex aggregates. Its second purpose is to reduce the overfitting introduc...

Diversified Random Forests Using Random Subspaces

Random Forest is an ensemble learning method used for classification and regression. In such an ensemble, multiple classifiers are used where each classifier casts one vote for its predicted class label. Majority voting is then used to determine the class label for unlabelled instances. Since it has been proven empirically that ensembles tend to yield better results when there is a significant ...

Variable selection using random forests

This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001, to investigate two classical issues of variable selection. The first one is to find important variables for interpretation and the second one is more restrictive and try to design a good prediction model. The main contribution is...