Three-dimensional electron-microscopic image stacks with almost isotropic resolution allow, for the first time, to determine the complete connection matrix of parts of the brain. In spite of major advances in staining, correct segmentation of these stacks remains challenging, because very few local mistakes can lead to severe global errors. We propose a hierarchical segmentation procedure based...

In this paper I present an extended implementation of the Random ferns algorithm contained in the R package rFerns. It di ers from the original by the ability of consuming categorical and numerical attributes instead of only binary ones. Also, instead of using simple attribute subspace ensemble it employs bagging and thus produce error approximation and variable importance measure modelled afte...

This paper proposes an improved random forest algorithm for classifying text data. This algorithm is particularly designed for analyzing very high dimensional data with multiple classes whose well-known representative data is text corpus. A novel feature weighting method and tree selection method are developed and synergistically served for making random forest framework well suited to categori...

Sometimes input probability distributions for stochastic models are not so simple that standard distributions suit. In this case, we model with weighted sums of standard distributions. These composed distributions may have many parameters which must be estimated. This is not easy with common estimation methods like maximum-likelihood. We use the genetic algorithm for that. The design of the com...

Ensemble learning is a machine learning approach that utilises a number of classifiers to contribute via voting to identifying the class label for any unlabelled instances. Random Forests RF is an ensemble classification approach that has proved its high accuracy and superiority. However, most of the commonly used selection methods are static. Motivated by the idea of having self-optimised RF c...

This paper reviews the work done in various papers in the field of random forest to emphasize its importance as an important data analysis algorithm while comparing it with other algorithm at some places and sometimes using the algorithm with other methods for better accuracy at tree learning. It also studies the practical experiments done to establish the superiority of random forest over othe...

Search interface detection is an essential task for extracting information from the hidden Web. The challenge for this task is that search interface data is represented in high-dimensional and sparse features with many missing values. This paper presents a new multi-classifier ensemble approach to solving this problem. In this approach, we have extended the random forest algorithm with a weight...