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Cervical cancer is the third most common type of cancer in women worldwide. Most death cases of cervical cancer occur in less developed areas of the world. In this work, we develop an automated and low-cost method that is applicable in those low-resource regions. First, we propose a more distinctive multi-feature descriptor for encoding the cervical image information by enhancing an existing descriptor...
Individual credit risk evaluation is an important and challenging data mining problem in financial analysis domain. This paper compares the effectiveness of four data mining algorithms - logistic regression (LR), decision tree (C4.5), support vector machine (SVM) and neural networks (NN) by applying them to two credit data sets. Experiment results show that the LR and SVM algorithms produced the best...
This paper presents an algorithm using discriminative sparse representations to segment tissues in optical images of the uterine cervix. Because of the large variations in the image appearance caused by the changing of illumination and specular reflection, the different classes of color and texture features in optical images are often overlapped with each other. Using sparse representations they can...
In this paper, we introduce a new classifier ensemble approach, applied to tissue segmentation in optical images of the uterine cervix. Ensemble methods combine the predictions of a set of diverse classifiers. The main contribution of our approach is an effective way of combination based on each classifier's performance level-namely, the sensitivity p and specificity q, which also produces an optimal...
We empirically evaluate a distance-guided learning method embedded in a multiple classifier system (MCS) for tissue segmentation in optical images of the uterine cervix. Instead of combining multiple base classifiers as in traditional ensemble methods, we propose a Bhattacharyya distance based metric for measuring the similarity in decision boundary shapes between a pair of statistical classifiers...
In image classification problems, especially those involving tumor or precancerous lesion, we are usually faced with the situation in which the cost of mistakenly classifying samples in one class is much higher than that of the opposite mistake in the other class. Therefore it is essential to include cost information about classes in our classification methods. This paper applies a cost-sensitive...
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