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Training classifiers on skewed data can be technically challenging tasks, especially if the data is high-dimensional simultaneously, the tasks can become more difficult. In biomedicine field, skewed data type often appears. In this study, we try to deal with this problem by combining asymmetric bagging ensemble classifier (asBagging) that has been presented in previous work and an improved random...
Most of microarray data sets are imbalanced, i.e. the number of positive examples is much less than that of negative, which will hurt performance of classifiers when it is used for tumor classification. Though it is critical, few previous works paid attention to this problem. Here we propose embedded gene selection with two algorithms i.e. EGSEE (Embedded Gene Selection for EasyEnsemble) and EGSIEE...
This paper presents an implementation of incremental tumor diagnosis algorithm (ITDA) on microarray data for improving diagnostic accuracy of tumor. A classifier (BP or KNN) was used in the algorithm to estimate confidences of a new unlabeled sample in different classes. When one confidence is higher than the threshold, the sample will be labeled; otherwise, the sample will be diagnosed by medical...
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