Search

Purpose: To analyze the short- and long-term effect of perioperative blood transfusion (PBT) in patients undergoing surgical treatment for oral squamous cell carcinoma (SCC).Methods: Patients undergoing free flap reconstruction were retrospectively enrolled and divided into two groups based on the implementation of PBT. Flap revision, surgical site infection (SSI), flap failure, overall survival (OS), and disease-specific survival (DSS) were compared between the two groups.Results: In 170 patients with PBT, 10 (5.9%) flaps required exploration revision, SSI occurred in 18 (10.6%) patients, and flap necrosis was noted in 6 (3.5%) patients. These rates were comparable to those in patients without PBT. The two groups had similar DSS rates, but the 5-year OS rates were 49 and 59% in patients with PBT and without PBT, respectively. This difference was significant. Patients with 4 units of PBT had OS rates comparable to those of patients with >4 units of PBT. A Cox model confirmed the fact that the decrease in OS was independent of PBT.Conclusion: In patients with free flap reconstruction for oral SCC, PBT did not increase the short-term complication rate or cancer-linked mortality. However, it was related to an elevated overall risk of death.

SOCSVM, as the derivative algorithm of OCSVM, is powerful in One-Class Classification. But in big data era, the computing and the storage requirement increase rapidly with the number of training vectors, putting many practical problems out of reach. For applying SOCSVM to large scale data mining, parallel SOCSVM is proposed by means of the idea of MapReduce and the iterative Hadoop model. The experiment results show SOCSVM on Hadoop is efficiency on practical problems.

Differed from the bounding hypersphere assumption in Support Vector Machine (SVM), Ellipsoidal Kernel Machine (EKM) adopts the compacter bounding ellipsoid assumption, and finds the separating plane inside the ellipsoid. It reduces the VC dimension in essence. However, EKM only applies in binary classification and does not work in outlier detection where generally only one class of samples existed. Thus, this paper proposes a method for outlier detection-One-class Ellipsoidal Machine and its kernel extension, which first finds a minimal ellipsoid enclosing all the input samples, and then finds the separating plane inside the ellipsoid by one-class SVM. Experiments on the artificial dataset and real datasets from UCI repository validate the effectiveness of the proposed method.

Recently, automatic document classification has attracted a lot of attentions due to the large quantity of web documents. Amongst, a special case is to distinguish whether a document belongs to a target class (Directory) when only the documents of target class are given, which is a standard oneclass classification problem. Moreover, differed from other data, web pages have intrinsic (text) and extrinsic(hyperlink) features. Thus they are very suitable for multiview learning. To tackle the task of one-class document classification, a multiview one-class classifier isproposed, it utilizes the One-cluster clustering based data description (OCCDD) as the base one-class classifier, then gets a one-class classifier in each view by setting a membership threshold, simultaneously, achieves the consensus of different views by a regularization term.Hereafter, different views boost each other, rather than ensemble the results independently orperform document recognition in single view case. We conduct the experiments on the standard WebKBdataset with OCCDD and the proposed multiview method. Experimental results show the good performance of the multiview method in terms of effectiveness and stability to parameter.

The one class classification problem aims to distinguish a target class from outliers. Two popular algorithms, one-class SVM (OCSVM) and single-class MPM (SCMPM), solve this problem by finding a hyperplane with the maximum distance to the origin. Their essential difference is that OCSVM focuses on the support vectors (SV) in a local manner while SCMPM emphasizes the whole datapsilas distribution using global information. In fact, these two seemingly different yet complementary characteristics are all important prior knowledge for the one-class-classifier (OCC) design. In this paper, we propose a novel OCC called global & local (GLocal) OCC, which incorporates the global and local information in a unified framework. Through embedding the samplespsila distribution information into the original OCSVM, the GLocal OCC provides a general way to extend the present SVM algorithm to consider global information. Moreover, the optimization problem of the GLocal OCC can be solved using the standard SVM approach similar to OCSVM, and preserves all the advantages of SVM. Experiment results on benchmark data sets show that the GLocal OCC really has better generalization compared with OCSVM and SCMPM.