Support vector machine classification for large data sets via minimum enclosing ball clustering

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.

The classification and diagnosis of erythrocytosis

An absolute erythrocytosis is present when the red cell mass is raised and the haematocrit is elevated above prescribed limits. Causes of an absolute erythrocytosis can be primary where there is an intrinsic problem in the bone marrow and secondary where there an event outside the bone marrow driving erythropoiesis. This can further be divided into congenital and acquired causes. There remain an unexplained group idiopathic erythrocytosis. Investigation commencing with thorough history taking and examination and then investigation depending on initial features is required. Clear simple criteria for polycythaemia vera are now defined. Those who do not fulfil these criteria require further investigation depending on the clinical scenario and initial results. The erythropoietin level provides some guidance as to the direction in which to proceed and the order and extent of investigation necessary in an individual patient. It should thus be possible to make an accurate diagnosis in the majority of patients.