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Table of Contents
by M.A. van Wyk and B.J. van Wyk (~220 kB)
An Adaptive Strategy for the Classification of G-Protein Coupled Receptors
A Comparison of Data-Driven and Model-Driven Approaches to Brightness Temperature Diurnal Cycle Interpolation
Data Characteristics that Determine Classifier Performance
A Simplified Volume under the ROC Hypersurface
A Current Analysis and Correction System for Vibration Forces on the Rotor of a Rotating Active Magnetic Bearing System
An Adaptive Strategy for the Classification of G-Protein Coupled Receptors By S. Mohamed, D. Rubin and T. Marwala
Abstract: One of the major problems in computation biology is the inability of existing classification models to incorporate expanding and new domain knowledge. The problem of static classification models is addressed in this paper by the introduction of incremental learning for problems in bioinformatics. Many machine learning tools have been applied to this problem using static machine learning structures such as neural networks or support vector machines that are unable to accommodate new information into their existing models. We utilize the ARTMAP as an alternate machine learning system that has the ability of incrementally learning new data as it becomes available. The fuzzy ARTMAP is found to be comparable to many of the widespread machine learning systems. The use of an evolutionary strategy in selection and combination of individual classifiers into an ensemble system, coupled with the incremental learning ability of fuzzy ARTMAP is proven to be suitable as a pattern classifier. The algorithm presented is tested using data from the G-Coupled Protein Receptors Database and shows good accuracy of 83 %. The system presented is also generally applicable, and can be used in problems in genomics and proteomics.
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A Comparison of Data-Driven and Model-Driven Approaches to Brightness Temperature Diurnal Cycle Interpolation by F. van den Bergh, M.A. van Wyk, B.J. van Wyk and G. Udahemuka
Abstract: This paper presents two new schemes for interpolating missing samples in satellite diurnal temperature cycles. The first scheme, referred to here as the cosine model, is an improvement model proposed by G?ttsche et al and combines a cosine and exponential function for modelling the diurnal temperature cycle. The second scheme employs a Reproducing Kernal Hilbert Space interpolator for interpolating the missing samples. The application of reproducing kernel Hilbert space interpolators to the diurnal temperature cycle interpolation problem is novel. Results obtained by means of computer experiments are presented.
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Data Characteristics that Determine Classifier Performance by C.M. van der Walt and E. Barnard
Abstract: We study the relationship between the distribution of data, on the one hand, and classifier performance, on the other, for non-parametric classifiers. It is shown that predictable factors such as the available amount of training data (relative to the dimensionality of the feature space), the spatial variability of the effective average distance between data samples, and the type and amount of noise in the data set influence such classifiers to a significant degree. The methods developed here can be used to gain a detailed understanding of classifier design and selection.
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A Simplified Volume under the ROC Hypersurface by T.C.W. Landgrebe and R.P.W. Duin
Abstract: The Receiver Operator Characteristic plot allows a classifier to be evaluated and optimised over all possible operating points. The area under the ROC has become a standard performance evaluation criterion in two-class pattern recognition problems, used to compare different classification algorithms independently of operating points, priors, and costs. Extending this measure to the multiclass case is considered in this paper, called the volume under the ROC hypersurface. A simplified measure is derived that ignores specific intra-class dimensions, and regards inter-class performances only. It is shown that this measure generalises from the 2-class case, but the bounds between random and perfect classification differ, with the lower bound tending towards zero as the dimensionality increases. A number of experiments with known distributions are used to verify the bounds, and to investigate a numerical integration approach to estimate the volume. Experiments on real data compare several competing classifiers in terms of both error-rate and the volume. It was found that some classifiers compete in terms of error-rate, but have significantly different volume scores, illustrating the importance of the approach.
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A Current Analysis and Correction System for Vibration Forces on the Rotor of a Rotating Active Magnetic Bearing System by R. Gouws and G. van Schoor
Abstract: In this paper, an on-line detection, correction and identification system for vibration forces on the rotor of a rotating active magnetic bearing is proposed. The on-line detection system performs current analysis by comparing ideal no fault currents with fault currents. Pattern recognition was performed by using an input feature obtained from the Wigner-Ville distribution and by comparing fault current patterns with historical fault database patterns obtained from a fully suspended 250 kW water cooling active magnetic bearing pump. A fuzzy logic system used the output of the pattern recognition process to perform error correction on the active magnetic bearing system. A fault identification system provides the type of fault, where the fault occurred in the active magnetic bearing system, the current state of the rotor and the parameters of the fault. Experiments were performed on a double radial active magnetic bearing test rack to demonstrate the effectiveness of the proposed system in the detection, correction and identification of vibration forces on the rotor of an active magnetic bearing system. The detection, diagnosis and correction system corrected and minimised vibration forces to a stable working condition.
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