Sparse Bayesian variable selection in multinomial probit regression model with application to high-dimensional data classification

Here we consider a multinomial probit regression model where the number of variables substantially exceeds the sample size and only a subset of the available variables is associated with the response. Thus selecting a small number of relevant variables for classification has received a great deal of attention. Generally when the number of variables is substantial, sparsity-enforcing priors for the regression coefficients are called for on grounds of predictive generalization and computational ease. In this paper, we propose a sparse Bayesian variable selection method in multinomial probit regression model for multi-class classification. The performance of our proposed method is demonstrated with one simulated data and three well-known gene expression profiling data: breast cancer data, leukemia data, and small round blue-cell tumors. The results show that compared with other methods, our method is able to select the relevant variables and can obtain competitive classification accuracy with a small subset of relevant genes.     

基于动态分析的中国知识密集型服务业概念与分类研究

知识密集型服务业作为现代服务业中最具发展活力与创新动力的部门,在提升企业竞争力、推动经济发展过程中发挥着日益重要的作用.通过对已有知识密集型服务业概念及分类的差异比较,分析其存在的缺陷并探究造成此缺陷的深层次原因,进而阐述中国知识密集型服务业的发展现状.最后,在把握知识密集型服务业不断成长的动态特质基础上,揭示中国知识密集型服务业的四大特征与概念及其具体分类.     

ABC Classification: Service Levels and Inventory Costs

ABC inventory classifications are widely used in practice, with demand value and demand volume as the most common ranking criteria. The standard approach in ABC applications is to set the same service level for all stock keeping units (SKUs) in a class. In this paper, we show (for three large real life datasets) that the application of both demand value and demand volume as ABC ranking criteria, with fixed service levels per class, leads to solutions that are far from cost optimal. An alternative criterion proposed by Zhang et al. performs much better, but is still considerably outperformed by a new criterion proposed in this paper. The new criterion is also more general in that it can take criticality of SKUs into account. Managerial insights are obtained into what class should have the highest/lowest service level, a topic that has been disputed in the literature.     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

基于样本集的区间数灰靶分类决策模型及应用

基于灰靶思想的不确定背景分类决策问题分析,本文提出了区间数灰靶分类决策模型。该模型将灰靶决策拓展到决策信息为区间数的情况,提出了区间数的靶心距测度方法;根据靶心距提出了灰靶分类决策中的靶心分类临界值设置方法;以决策对象的靶心距与临界值之间的偏差总量最小为目标,建立了指标权重和分类临界值的确定模型;依据求解的权重与分类临界值对决策对象集进行分类评价。算例分析验证了该模型的有效性和可行性,可以很好地解决决策对象众多、分类数不确定等特性的多属性分类决策问题。     

Feature Selection for Consistent Biclustering via Fractional 0–1 Programming

Biclustering consists in simultaneous partitioning of the set of samples and the set of their attributes (features) into subsets (classes). Samples and features classified together are supposed to have a high relevance to each other which can be observed by intensity of their expressions. We define the notion of consistency for biclustering using interrelation between centroids of sample and feature classes. We prove that consistent biclustering implies separability of the classes by convex cones. While previous works on biclustering concentrated on unsupervised learning and did not consider employing a training set, whose classification is given, we propose a model for supervised biclustering, whose consistency is achieved by feature selection. The developed model involves solution of a fractional 0–1 programming problem. Preliminary computational results on microarray data mining problems are reported.This research work was partially supported by NSF, NIH and AirForce grants.     

基于ARTⅡ网络的煤炭资源资产分类研究

本文建立了用于煤炭资源资产分类的ARTⅡ神经网络模型,编制了相应的计算机和软件,并将ARTⅡ模型与模糊分类模型和基于BP网络的分类模型进行了对比分析,实例运行结果表明,用ARTⅡ网络进行分类具有分类稳定、结果可靠等特点。     

Inductive Inference: An Axiomatic Approach

A predictor is asked to rank eventualities according to their plausibility, based on past cases. We assume that she can form a ranking given any memory that consists of finitely many past cases. Mild consistency requirements on these rankings imply that they have a numerical representation via a matrix assigning numbers to eventuality–case pairs, as follows. Given a memory, each eventuality is ranked according to the sum of the numbers in its row, over cases in memory. The number attached to an eventuality–case pair can be interpreted as the degree of support that the past case lends to the plausibility of the eventuality. Special instances of this result may be viewed as axiomatizing kernel methods for estimation of densities and for classification problems. Interpreting the same result for rankings of theories or hypotheses, rather than of specific eventualities, it is shown that one may ascribe to the predictor subjective conditional probabilities of cases given theories, such that her rankings of theories agree with rankings by the likelihood functions.     

浅谈藏文古文献的开发利用

本文论述了藏文古文献常见金石铭刻、木简、文书写卷、木刻版文献这四种类型以及各种类型的渊源,在不同历史条件下所起的作用。抢救和保护藏文古文献是目前当务之急。在藏文古文献的整理中,应重视文献的永在价值和潜在价值。藏文文献的开发利用应从宣传介绍、吸引更多的人重视和利用藏文古文献、改进服务方式、建立藏文古文献开发利用的领导机构、培养古文献整理人才几方面着手,使藏文古文献在藏学研究中发挥出更大的作用