In this paper we combine an integer programming approach and a computer simulation tool to successfully develop and verify an improved classification schedule for a real-world train classification instance. First, we derive an integer program for computing train classification schedules based on an earlier developed bitstring representation of such schedules. We show how to incorporate various ...

In this paper we combine an integer programming approach and a computer simulation tool to successfully develop and verify an improved classification schedule for a real-world train classification instance. First, we derive an integer program for computing train classification schedules based on an earlier developed bitstring representation of such schedules. We show how to incorporate various ...

Energy simulation tool is a tool to simulate energy use by a building prior to the erection of the building. Commonly it has a feature providing alternative designs that are better than the user’s design. In this paper, we propose a novel method in searching alternative design that is by using classification method. The classifiers we use are Naïve Bayes, Decision Tree, and k-Nearest Neighbor. ...

There are many applications of multilayer neural networks to pattern classification problems in the engineering field. Recently, it has been shown that Bayes a posteriori probability can be estimated by feedforward neural networks through computer simulation. In this paper, Bayes decision theory is combined with the approximation theory on three-layer neural networks, and the two-category n-dim...

This paper describes a modular simulation framework that can be used for testing of “intelligent” reconnaissance sensor systems in various environments, weather conditions and time of day. The simulation framework is called MSSLab (Multi-Sensor Simulation Lab) and it allows for high fidelity sensor data simulation. One objective to develop this framework is to facilitate development, testing an...

The quality of Support Vector Machines (SVM) binary classification of spatial environmental data is evaluated with geostatistical nonparametric conditional stochastic simulations a spatial Monte Carlo model based on sequential indicator simulation algorithm. Equally probable realizations are generated and compared with SVM classification. Uncertainty of predictions is described by conditional s...

Model-based development uses modeling and simulation as essential means for specification, rapid prototyping, design, and realization of embedded systems. The classification-tree method complements model-based development with a formal approach for test case description and automation. This paper shows how “raw” classification trees are transformed into complete classification trees using an ex...

In this master thesis some properties of bags of imprecise classification trees, as introduced in Abellán and Masegosa (2010), are analysed. In the beginning the statistical background of imprecise classification trees is outlined – starting with an overview on measuring uncertainty within the concept of Dempster–Shafer theory is presented, followed by a discussion of its application in a tree–...