In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

/pdf/evolutionary-optimization-of-radial-basis-function-mbyw310yx0.pdf

Evolutionary optimization of radial basis function classifiers for data mining applications

Finding optimal parameters for a model is usually a crucial task in engineering approaches to classification and modeling tasks. An automated approach is particularly desirable when a hybrid approach combining several distinct methods is to be used. In this work we present an algorithm for finding optimal parameters that works with no specific information about the underlying model and only requires the discretization of the parameter range to be considered. We will illustrate the procedure’s performance for multilayer perceptrons and support vector machines, obtaining competitive results with state-of-the-art procedures whose parameters have been tuned by experts. Our procedure is much more efficient than straight parameter search (and probably than other procedures that have appeared in the literature), but it may nevertheless require extensive computations to arrive at the best parameter values, a potential drawback that can be overcome in practice because of its highly parallelizable nature.

Finding Optimal Model Parameters by Discrete Grid Search

An Indexed Bibliography of Genetic Algorithms and Neural Networks

The objective of this work has been to develop layers of control and optimization modules for the purpose of urban traffic management. The authors utilize the semantic control paradigm to model both the macrolevel (traffic control) and the microlevel (vehicle path planning and steering control). A semantic controller consists of three modules for identification, goal selection, and adaptation, respectively. This hierarchical structure has been used successfully at the Center for Optimization and Semantic Control to solve complex, non-linear, and time-varying problems. The focus of this paper is the identifier module, which performs "system identification," i.e., determines the road network congestion level. Traffic flow can be characterized as a nonlinear stochastic process where linear prediction models such as linear regression are not suitable. However, neural network techniques may provide an effective tool for data-based modeling and system identification.

Traffic Prediction and Management via RBF Neural Nets and Semantic Control

This paper proposes a hybrid model developed through wiser integration of wavelet transforms, floating point GA and artificial neural networks for prediction of short-term load The use of wavelet transforms has added the capability of capturing of both global trend and hidden templates in loads, which is otherwise very difficult to incorporate into the prediction model of ANN Auto-configuring RBF networks are used for predicting the wavelet coefficients of the future loads Floating point GA (FPGA) is used for optimizing the RBF networks The use of GA optimized RBF network has added to the model the online prediction capability of short-term loads accurately The performance of the proposed model is validated using Queensland electricity demand data from the Australian National Electricity Market Results demonstrate that the proposed model is more accurate as compared to RBF only model

/pdf/wavelet-ga-ann-based-hybrid-model-for-accurate-prediction-of-43ac8szzfk.pdf

Wavelet-GA-ANN Based Hybrid Model for Accurate Prediction of Short-Term Load Forecast

Radial basis function (RBF) networks are an attractive tool for modeling dynamic systems for control purposes. This paper presents a new methodology to find the optimum width parameters in the RBF network model. This methodology, which combines genetic algorithms and the orthogonal least squares method, is described in detail. Finally, two examples illustrate the usefulness of this method.

Using genetic algorithms to estimate the optimum width parameter in radial basis function networks

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Using genetic algorithms to estimate the optimum width parameter in radial basis function networks