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This paper describes techniques for integrating neural networks and symbolic components into powerful hybrid systems. Neural networks have unique processing characteristics that enable tasks to be performed that would be diicult or intractable for a symbolic rule-based system. However, a stand-alone neural network requires an interpretation either by a h(More)
Feature selection is a common and key problem in many classification and regression tasks. It can be viewed as a multiobjective optimisation problem, since, in the simplest case, it involves feature subset size minimisation and performance maximisation. This paper presents a multiobjective evolutionary approach for feature selection. A novel(More)
1 Abstract Recently there has been a lot of interest in the extraction of symbolic rules from neural networks. The work described in this paper is concerned with an evaluation and comparison of the accuracy and complexity of symbolic rules extracted from radial basis function networks and multi-layer perceptrons. Here we examine the ability of rule(More)
Empirical modelling in high dimensional spaces is usually preceded by a feature selection stage. Irrelevant or noisy features unnecessarily increase the complexity of the problem and can degrade modelling performance. Here, multiobjective genetic algorithms are proposed as effective means of evolving a diverse population of alternative feature sets with(More)
Computerized registration and randomization for a cooperative clinical trials group is a useful addition to its data gathering and managing process. An automated system eliminates unnecessary paperwork, allows more sophisticated randomization algorithms to be implemented, and makes available a variety of computer-generated reports such as confirmation of(More)
Using primary embryonic Drosophila cell cultures, we have investigated the assembly of transcellular microtubule bundles in epidermal tendon cells. Muscles attach to the tendon cells of previously undescribed epidermal balls that form shortly after culture initiation. Basal capture of microtubule ends in cultured tendon cells is confined to discrete sites(More)
Extracting rules from RBFs is not a trivial task because of nonlinear functions or high input di-mensionality. In such cases, some of the hidden units of the RBF network have a tendency to be " shared " across several output classes or even may not contribute to any output class. To address this we have developed an algorithm called LREX (for Local Rule(More)
This paper examines the performance of seven neural network architectures in classifying and detecting novel events contained within data collected from turbine sensors. Several different multi-layer perceptrons were built and trained using back propagation, conjugate gradient and Quasi-Newton training algorithms. In addition, Linear networks, Radial Basis(More)
Radial basis neural (RBF) networks provide an excellent solution to many pattern recognition and classiication problems. However, RBF networks are also a local representation technique that enables the easy conversion of the hidden units into symbolic rules. This paper examines rules extracted from RBF networks. We use the iris ower classi-cation task and a(More)