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For customer data mining of Google Merchandise Store, ensemble learning models such as LightGBM are popular. However, LightGBM mines the data information once, which has the rough granularity of data mining. So that LightGBM cannot dig into the more potential internal correlation information of Google Merchandise Store’s dataset. In this paper, the deep LGB model is proposed to automatically refine the granularity of data mining through sliding window, on this basis, the model is endowed with certain representation learning ability through Deep, so as to dig out deeper association between data. Then, a semi-automatic feature engineering is proposed, which firstly processes some features of the data set automatically, and then generates the final data set with a little manual analysis. The experimental results show that, use customer data of the Google Merchandise Store, the prediction accuracy of the deep LGB model with semi-automatic feature engineering is 6.16% points higher than that of the original data set put into the single LGB model.

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.

To determine the short and mid-term effect of paravertebral adriamycin injection under CT guidance on intractable postherpetic neuralgia (PHN).From January 2010 to May 2012, 68 patients with intractable PHN receiving paravertebral adriamycin injection under CT guidance were enrolled. The outcome included the Quality of Life Score (QLS), Visual Analogue Score (VAS) (average, the worst and the least VAS), pain relief rate at the time before and after the injection, and 1 year after discharge.Lower VAS was observed after the injection than that before the injection [(3.5 ± 1.5) vs (7.9 ± 1.3) on average; (2.1 ± 0.9) vs (6.5 ± 1.7) at least and (4.5 ± 1.4) vs (9.2 ± 1.1) at worst, P0.05]. Lower VAS at 1 year after discharge was found than that before the injection [(2.2 ± 1.8) vs (7.9 ± 1.3) on average; (1.5 ± 0.8) vs (6.5 ± 1.7) at least; (3.2 ± 1.6) vs (9.2 ± 1.1) at worst ] and that after the injection [(3.5 ± 1.5) vs (2.2 ± 1.8) on average; (1.5 ± 0.8) vs (2.1 ± 0.9) at least and (3.2 ± 1.6) vs (4.5 ± 1.4) at worst, P0.05]. The pain relief rate after the injection was (68 ± 23)%. The pain relief rate at 1 year after discharge (81 ± 22)% was higher than that at discharge (68 ± 23)% (P0.05). The quality of life evaluation index scale after the injection (daily life, diet, general activities, sleep, work and social activities) was significantly improved than that before the injection [(10.1 ± 2.2) vs (14.2 ± 1.9), P0.05]. The quality of life evaluation index scale at 1 year after discharge was significantly improved than that before the injection [(7.0 ± 2.1) vs (14.2 ± 1.9)] and that after the injection [(7.0 ± 2.1) vs (10.1 ± 2.2), P0.05]. No complication was observed when the patients were discharged and 1 year after discharge.Paravertebral adriamycin injection under CT guidance for intractable PHN can effectively relieve the patient's pain, improve the quality of sleep and life without obvious complications. It is safe and effective for intractable PHN.

To investigate the effect of pre-amputation pain block on the N-methyl-D-aspartate receptor activation in the central nervous system of amputated rats, and the association between pre-amputation pain block and chronic amputation-related pain.Thirty-six adult male SD rats were randomly assigned to an NA group (n=12), a PA group (n=12) and a PAB group (n=12). Group NA was intraplantarly injected saline l00 μL while group PA and group PAB were intraplantarly injected complete Freund adjuvant (CFA) 100 μL. The sciatic nerve of group NA and group PA were freed from surrounding tissue, and that of group PAB was blocked by bupivacaine under pentobarbital sodium anesthesia 5 days after the injection. Thermal withdrawal latency (TWL) was measured before and after the injection. All rats were amputated at the scheduled survival time. The expression of N-methyl-D-aspartate receptor (NR2B) was measured by immunohistochemistry in L4-6 of the spinal cord and the anterior cingulated cortex 7 days after the amputation procedure.The TWL after intraplantar administration of CFA in group PA and group PAB decreased significantly compared with the baseline value (P0.05), while the saline treated control group remained unchanged. Besides the basic value, the TWL of group PA was shorter than that of group NA at the above-mentioned time-points (P0.05). Compared with the basic value and group NA, the TWL of group PAB after the block increased significantly (P0.05). Compared with group NA and group PAB, group PA had a remarkably high expression of NR2B (P0.05), while there was no difference between group PA and group PAB.Pre-amputation pain may activate N-methyl-D-aspartate receptor of the central nervous system, which may lead acute postoperative pain to chronic pain. It is necessary to treat pre-amputation pain.

To explore the clinical relevance of inhibition of multidrug resistance transporter 1 and organic anion transporting polypeptide transporter, a drug-drug interaction study was conducted using aliskiren and cyclosporine. This was an open-label, single-sequence, parallel-group, single-dose study in healthy subjects. Subjects (n = 14) first received aliskiren 75 mg orally (period 1), followed by aliskiren 75 mg + cyclosporine 200 mg (period 2) after a 7-day washout period, and aliskiren 75 mg + cyclosporine 600 mg (period 3) after a 14-day washout period. Safety and pharmacokinetics were analyzed during each period. The primary objective was to characterize pharmacokinetics of aliskiren (single-dose and combination with cyclosporine). The increases in area under the time-concentration curve from time 0 to infinity and maximum concentration associated with cyclosporine 200 mg or 600 mg were 4- to 5-fold and 2.5-fold, respectively. Mean half-life increased from 25 to 45 hours. Based on comparison to literature, a single-dose of aliskiren 75 mg did not alter the pharmacokinetics of cyclosporine. Aliskiren 75 mg was well tolerated. Combination with cyclosporine increased the number of adverse events, mainly hot flush and gastrointestinal symptoms, with no serious adverse events. Two adverse events led to withdrawal (ligament rupture, not suspected to be study-drug related; and vomiting, suspected to be study-drug related). Laboratory parameters, vital signs, and electrocardiographs showed no time- or treatment-related changes. As cyclosporine significantly altered the pharmacokinetics of aliskiren in humans, its use with aliskiren is not recommended.

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.