Proceedings of the 2005 International Conference on Simulation and Modeling

This paper proposes a coevolutionary classification method to discover classifiers for multidimensional pattern classification problems with continuous input variables. The classification problems may be decomposed into two sub-problems, which are feature selection and classifier adaptation. A coevolutionary classification method is designed by coordinating the two sub-problems, whose performances are affected by each other. The proposed method establishes a group of partial sub-regions, defined by regional variable set, and then fits a finite number of classifiers to the data pattern by combining a genetic algorithm and a local adaptation algorithm in every subregion. A cycle of the cooperation loop is completed by evolving the sub-regions based on the evaluation results of the fitted classifiers located in the corresponding subregions. The classifier system has been tested with wellknown data sets from the UCI machine-learning database, showing superior performance to other methods such as the nearest neighbor, decision tree, and neural networks

AGING TEST AND SOFTWARE RELIABILITY ANALYSIS METNOD FOR PC-BASED CONTROLLER

This paper presents a survey of software reliability modeling and it's application to pre-built software system combined with hardware such as numerical controller based on personal computer systems. Many a systems in these days are much more becoming software intensive and many software intensive systems are safety critical. For this reason, the technique well developed to measure of software reliability is very important for whom to assess such a system. This paper provides a brief idea of method to evaluat

Proceedings of the 2005 International Conference on Simulation and Modelling

Most documented Bayesian network (BN) applications have been built through knowledge elicitation from domain experts (DEs). The difficulties involved have led to growing interest in machine learning of BNs from data. There is a further need for combining what can be learned from the data with what can be elicited from DEs. In previous work, we proposed a detailed methodology for this combination, specifically for the parameters of a BN. In this paper, we illustrate the techniques using a case study of a