In recent years, the dramatic rise in the use of the web and the improvement in
communications in general have transformed our society into one that strongly depends on …

Much of previous attention on decision trees focuses on the splitting criteria and optimization
of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom …

General regression and classification models are constructed as linear combinations of
simple rules derived from the data. Each rule consists of a conjunction of a small number of …

The Support Vector Machine is a powerful new learning algorithm for solving a variety of
learning and function estimation problems, such as pattern recognition, regression …

Working set selection is an important step in decomposition methods for training support
vector machines (SVMs). This paper develops a new technique for working set selection in …

An algorithm is proposed which generates a nonlinear kernel-based separating surface that
requires as little as 1% of a large dataset for its explicit evaluation. To generate this …

Recent studies have shown that the random subspace method can be used to create
multiple independent tree-classifiers that can be combined to improve accuracy. We apply …

The decomposition method is currently one of the major methods for solving support vector
machines. An important issue of this method is the selection of working sets. In this paper …

Granular ball computing (GBC), as an efficient, robust, and scalable learning method, has
become a popular research topic of granular computing. GBC includes two stages: granular …

We will introduce a generic approach for solving problems in pattern recognition based on
the synthesis of accurate multiclass discriminators from large numbers of very inaccurate" …