The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption is not always fulfilled. An alternative approach is random forests for survival outcomes. The standard split criterion for random survival forests is the log-rank test statistics, which favors splitting variables with many possible sp...

The Darwinian concept of evolution provides a usef3 theoretical construct to consider the ongoing development of forest management and forest operations. For example, changing environments, “survival of the fittest”, and random mutation are processes, which have analogs within the forestry arena. The success and survival of any forest operations technology is determined by how well it meets the...

We propose a unified and flexible framework for ensemble learning in the presence of censoring. For right-censored data, we introduce a random forest algorithm and a generic gradient boosting algorithm for the construction of prognostic and diagnostic models. The methodology is utilized for predicting the survival time of patients suffering from acute myeloid leukemia based on clinical and gene...

Network intrusion detection has the problems of large amounts data, numerous attributes, and different levels importance for each attribute in detection. However, random forests, results have deviations due to selection attributes. Therefore, aiming at current problems, considering increasing probability essential features being selected, a network model based on three-way selected forest (IDTS...

This paper analyzes the risk of mortgage default and prepay for single-family, 30year fixed rate mortgages using a variety of machine learning and survival analysis methods. Predictions are made for homeowner choices to continue payment, default, or prepay using both parametric and non-parametric models. These models include Binary Logit, Multinomial Logit, K-Nearest Neighbors, K-fold Cross Val...

Extracting digital elevationmodels (DTMs) from LiDAR data under forest canopy is a challenging task. This is because the forest canopy tends to block a portion of the LiDAR pulses from reaching the ground, hence introducing gaps in the data. This paper presents an algorithm for DTM extraction from LiDAR data under forest canopy. The algorithm copes with the challenge of low data density by gene...

12 Promising results have been obtained in recent years in the use of high-resolution X-band stereo SAR 13 satellite images (with the spatial resolution being in order of meters) in the extraction of elevation 14 data. In the case of forested areas, the extracted elevation values appear to be somewhere between 15 the ground surface and the top of the canopy, depending on the forest characterist...