13,107 research outputs found
Classification of Product Images in Different Color Models with Customized Kernel for Support Vector Machine
Support Vector Machine (SVM) is widely
recognized as a potent data mining technique for solving
supervised learning problems. The technique has practical
applications in many domains such as e-commerce product
classification. However, data sets of large sizes in this
application domain often present a negative repercussion for
SVM coverage because its training complexity is highly
dependent on input size. Moreover, a single kernel may not
adequately produce an optimal division between product
classes, thereby inhibiting its performance. The literature
recommends using multiple kernels to achieve flexibility in the
applications of SVM. In addition, color features of product
images have been found to improve classification performance
of a learning technique, but choosing the right color model is
particularly challenging because different color models have
varying properties. In this paper, we propose color image
classification framework that integrates linear and radial basis
function (LaRBF) kernels for SVM. Experiments were
performed in five different color models to validate the
performance of SVM based LaRBF in classifying 100 classes of
e-commerce product images obtained from the PI 100
Microsoft corpus. Classification accuracy of 83.5% was
realized with the LaRBF in RGB color model, which is an
improvement over an existing method
Customizing kernel functions for SVM-based hyperspectral image classification
Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms. However, few efforts have been made to extend SVMs to cover the specific requirements of hyperspectral image classification, for example, by building tailor-made kernels. Observation of real-life spectral imagery from the AVIRIS hyperspectral sensor shows that the useful information for classification is not equally distributed across bands, which provides potential to enhance the SVM's performance through exploring different kernel functions. Spectrally weighted kernels are, therefore, proposed, and a set of particular weights is chosen by either optimizing an estimate of generalization error or evaluating each band's utility level. To assess the effectiveness of the proposed method, experiments are carried out on the publicly available 92AV3C dataset collected from the 220-dimensional AVIRIS hyperspectral sensor. Results indicate that the method is generally effective in improving performance: spectral weighting based on learning weights by gradient descent is found to be slightly better than an alternative method based on estimating ";relevance"; between band information and ground trut
Early hospital mortality prediction using vital signals
Early hospital mortality prediction is critical as intensivists strive to
make efficient medical decisions about the severely ill patients staying in
intensive care units. As a result, various methods have been developed to
address this problem based on clinical records. However, some of the laboratory
test results are time-consuming and need to be processed. In this paper, we
propose a novel method to predict mortality using features extracted from the
heart signals of patients within the first hour of ICU admission. In order to
predict the risk, quantitative features have been computed based on the heart
rate signals of ICU patients. Each signal is described in terms of 12
statistical and signal-based features. The extracted features are fed into
eight classifiers: decision tree, linear discriminant, logistic regression,
support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and
K-nearest neighborhood (K-NN). To derive insight into the performance of the
proposed method, several experiments have been conducted using the well-known
clinical dataset named Medical Information Mart for Intensive Care III
(MIMIC-III). The experimental results demonstrate the capability of the
proposed method in terms of precision, recall, F1-score, and area under the
receiver operating characteristic curve (AUC). The decision tree classifier
satisfies both accuracy and interpretability better than the other classifiers,
producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It
indicates that heart rate signals can be used for predicting mortality in
patients in the ICU, achieving a comparable performance with existing
predictions that rely on high dimensional features from clinical records which
need to be processed and may contain missing information.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE
2018 and published in Smart Health journa
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