In the paper the comparison of ensemble based methods applied to censored survival data was conducted. Bagging survival trees, dipolar survival tree ensemble and random forest were taken into consideration. The prediction ability was evaluated by the integrated Brier score, the prediction measure developed for survival data. Two real datasets with different percentage of censored observations w...

We study random cutting down of a rooted tree and show that the number of cuts is equal (in distribution) to the number of records in the tree when edges (or vertices) are assigned random labels. Limit theorems are given for this number, in particular when the tree is a random conditioned Galton–Watson tree. We consider both the distribution when both the tree and the cutting (or labels) are ra...

This paper addresses to a classification challenge in a filtering task. We use different kind of features to depict vital documents and filter them out from the stream. A vital document has to be relevant for a particular entity and has to relate a new story about it. We introduce different features that uses time as well as entity profil to perform classification. We evaluate our method on fra...

This research evaluates the utility and performance of a machine learning decision tree classification technique – random forests – for forest classification using remote sensing and ancillary spatial data, across a large area of heterogeneous forest ecosystems in Victoria, Australia. Random forest classification models for forest extent, type and height were trained using 786 2 km x 2 km aeria...

Relative importance of regressor variables is an old topic that still awaits a satisfactory solution. When interest is in attributing importance in linear regression, averaging over orderings methods for decomposing R2 are among the state-of-theart methods, although the mechanism behind their behavior is not (yet) completely understood. Random forests—a machinelearning tool for classification a...

Contour detection is an important and fundamental problem in computer vision which finds numerous applications. Despite significant progress has been made in the past decades, contour detection from natural images remains a challenging task due to the difficulty of clearly distinguishing between edges of objects and surrounding backgrounds. To address this problem, we first capture multi-scale ...

An automatic particle picking algorithm for processing electron micrographs of a large molecular complex, the 26S proteasome, is described. The algorithm makes use of a coherence enhancing diffusion filter to denoise the data, and a random forest classifier for removing false positives. It does not make use of a 3D reference model, but uses a training set of manually picked particles instead. F...

Model interpretation is one of the key aspects of the model evaluation process. The explanation of the relationship between model variables and outputs is relatively easy for statistical models, such as linear regressions, thanks to the availability of model parameters and their statistical significance. For “black box” models, such as random forest, this information is hidden inside the model ...

Leskovec, Kleinberg and Faloutsos (2005) observed that many social networks exhibit properties such as shrinking (i.e. bounded) diameter, densification, and (power-law) heavy tail degree distributions. To explain these phenomena, they introduced a generative model, called the Forest Fire model, and using simulations showed that this model indeed exhibited these properties; however, proving this...