Inductive algorithms rely strongly on their representational biases. Representational inadequacy can be mitigated by constructive induction. This paper introduces the notion of a relative gain measure and describes a new constructive induction algorithm (GALA) which is independent of the learning algorithm. GALA generates a small number of new boolean attributes from existing boolean, nominal o...

Most of the widely used pattern classification algorithms, such as Support Vector Machines (SVM), are sensitive to the presence of irrelevant or redundant features in the training data. Automatic feature selection algorithms aim at selecting a subset of features present in a given dataset so that the achieved accuracy of the following classifier can be maximized. Feature selection algorithms ar...

“Accessing information anytime, anywhere, with any device” is a widely used slogan. The problem, however, is how to bring multimedia services to terminals with limited capabilities. Our approach offers a solution for delivering multimedia presentations to terminals connected to mobile or fixed networks with varying capabilities. Our media wrapper is used to adapt the original presentation accor...

We present a novel approach to automatic information extraction from Deep Web Life Science databases using wrapper induction. Traditional wrapper induction techniques focus on learning wrappers based on examples from one class of Web pages, i.e. from Web pages that are all similar in structure and content. Thereby, traditional wrapper induction targets the understanding of Web pages generated f...

We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation...

After the general introduction a short view into today’s component-oriented usage-scenarios of object-relational databases is given and the resulting requirements for reusable object-relational mapping components are stated. Then a clarification of the term "component" is given. Followed by an overview of the UFO-RDB wrapper component and lists and short discussion of the provided features. The...

In recent years, distributed learning has been the focus of much attention due to the proliferation of big databases, usually distributed. In this context, machine learning can take advantage of feature selection methods to deal with these datasets of high dimensionality. However, the great majority of current feature selection algorithms are designed for centralized learning. To confront the p...

We present a language extension, which integrates in a Java like language a mechanism for dynamically extending object behaviors without changing their type. Our approach consists in moving the addition of new features from class (static) level to object (dynamic) level: the basic features of entities (representing their structure) are separated from the additional ones (wrapper classes whose i...

This paper presents a novel feature selection approach to deal with issues of high dimensionality in biomedical data classification. Extensive research has been performed in the field of pattern recognition and machine learning. Dozens of feature selection methods have been developed in the literature, which can be classified into three main categories: filter, wrapper and hybrid approaches. Fi...