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Improvement of classification accuracy is importance in data analysis problems. Enhancement of techniques have been proposed previously to address the problems as regard to classification performance, however, the issues of misclassification and noise elimination in the early stage of processing have been ignored by many researchers. If these problems were addressed, the performance of the classification...
The smart grid initiative requires self-healing distribution systems with more accurate fault detection and classification techniques. A multi-sensor feeder-level fault detection and classification algorithm is presented in this work, based on the techniques of the support vector machine and the principal components. An IEEE 34-bus feeder model with dynamic loading conditions is used to evaluate the...
A three year Knowledge Transfer Partnership between @UK plc, University of Reading and Goldsmiths College, London produced an e-procurement system called SpendInsight which the National Audit Office reports could save the NHS Σ500 million per annum. An extension to the system, GreenInsight, enables procurers to assess the environmental impact of their purchases and hence make savings there. Key to...
Medicine is one of the major fields where the application of artificial intelligence primarily deals with construction of programs that perform diagnosis and make therapy recommendations. In digital mammography, data mining techniques are used to detect and characterize abnormalities in images and clinical reports. In the existing approaches, the mammogram image classification is done in either clinical...
Recently, the classification study is accelerated, especially in machine learning expertise. Although the decision tree was still recommended as a classification tool in diagnosing electric power apparatus because of the property having the visible if-then rule, the recent development in classification methods, especially those using the ensemble methods, suggests us to apply these methods to condition...
According to the symmetric characteristics of bispectrum, a novel feature extraction scheme, which includes the summation-at-every-column feature vector, the summation-at-every-row feature vector and their combination in a triangle area, one of the 12 symmetric areas of bispectrum, is proposed. By using One-against-One (OAO) method of multi classification of Support Vector Machine (SVM), the mean...
A new method for detecting and classifying loudspeaker faults is presented in this paper. Total response of high-order harmonics groups is measured and used as defect features of loudspeaker. Based on support vector machine (SVM), we built a classification system combined with one-class SVM and Directed Acyclic Graphic SVM (DAGSVM). Comparing with K-nearest neighbor (k-NN) classifier, the accuracy...
The problem of classification on highly imbalanced datasets has been studied extensively in the literature. Most classifiers show significant deterioration in performance when dealing with skewed datasets. In this paper, we first examine the underlying reasons for SVM's deterioration on imbalanced datasets. We then propose two modifications for the soft margin SVM, where we change or add constraints...
Multichannel remote sensing (MRS) data can be passed to customers in different forms: original (raw), pre-filtered, compressed, classified. In this paper, we analyze how pre-filtering of original images can influence classification accuracy of three-channel images using three channels of real life Landsat TM data with simulated noise.
Microarray datasets are often limited to a small number of samples with a large number of gene expressions. Therefore, dimensionality reduction through a feature/gene selection process is highly important for classification purposes. In this paper, a feature perturbation method we previously introduced is applied to do gene selection from microarray data. A publicly available colon cancer dataset...
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