The least square twin support vector machine (LS-TSVM) obtains two non-parallel hyperplanes by directly solving two systems of linear equations instead of two quadratic programming problems (QPPs) as in the conventional twin support vector machine (TSVM), which makes the computational speed of LS-TSVM faster than that of the TSVM. However, LS-TSVM ignores the structural information of data which may contain some vital prior domain knowledge for training a classifier. In this paper, we apply the prior structural information of data into the LS-TSVM to build a better classifier, called the structural least square twin support vector machine (S-LSTSVM). Since it incorporates the data distribution information into the model, S-LSTSVM has good generalization performance. Furthermore, S-LSTSVM costs less time by solving two systems of linear equations compared with other existing methods based on structural information. Experimental results on twelve benchmark datasets demonstrate that our S-LSTSVM performs well. Finally, we apply it into Alzheimer's disease diagnosis to further demonstrate the advantage of our algorithm.
Acute-on-chronic liver failure (ACLF) is characterized by jaundice, coagulopathy, hepatic encephalopathy, and associated with high mortality. According to the progress of patients, we partition 81 ACLF patients into three groups. Group I includes 40 improved patients, group II contains 18 death patients, and group III is composed of 23 unlabeled patients. For the imbalanced characteristic of groups I and II, we construct an imbalanced prediction model based on small sphere and large margin approach (SSLM). SSLM classifies two classes of samples by maximizing their margin and then is an effective classification method for imbalanced data. For groups I, II and III, we present a prediction model based on semi-supervised twin support vector machine (TSVM), which integrates 23 unlabeled samples into the training process and improves testing accuracy. Compared with other three algorithms, our two proposed prediction models produce better testing accuracy. Finally we apply them to predict 23 not confirmed patients, and integrate them with the MELD method to obtain their prediction labels.
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