IRUSRT: A Novel Imbalanced Learning Technique by Combining Inverse Random Under Sampling and Random Tree

In this article, a novel technique IRUSRT (inverse random under sampling and random tree) by combining inverse random under sampling and random tree is proposed to implement imbalanced learning. The main idea is to severely under sample the majority class thus creating multiple distinct training sets. With each training set, a random tree is trained to separate the minority class from the majority class. By combining these random trees through fusion, a composite classifier is constructed. The experimental analysis on 23 real-world datasets assessed over area under the ROC curve (AUC), F-measure, and G-mean indicates that IRUSRT performs significantly better when compared with many existing class imbalance learning methods.     

明清时期华北宗族的发展——以山西洪洞刘氏为例

明清时期山西洪洞刘氏与当时南方的宗族一样,从明中后期开始组织化.刘氏从默默无闻的家族崛起,成为当地的望族,发展起宗族组织,并从明中叶维持到清末,主要原因在于科举的成功,农商结合的经济以及响应政府的教化活动,此外与大族联姻也起到一定的作用.洪洞刘氏虽然经历了明清鼎革,但是其宗族建设进程并未断裂,在清朝继续保持其优越的政治地位.刘氏宗族在清代通过进一步加强宗族建设,使宗族组织化,宗族经济增强,反映了明清华北宗族历史发展的连续性.     

Probability and Possibility‐Based Representations of Uncertainty in Fault Tree Analysis

Expert knowledge is an important source of input to risk analysis. In practice, experts might be reluctant to characterize their knowledge and the related (epistemic) uncertainty using precise probabilities. The theory of possibility allows for imprecision in probability assignments. The associated possibilistic representation of epistemic uncertainty can be combined with, and transformed into, a probabilistic representation; in this article, we show this with reference to a simple fault tree analysis. We apply an integrated (hybrid) probabilistic‐possibilistic computational framework for the joint propagation of the epistemic uncertainty on the values of the (limiting relative frequency) probabilities of the basic events of the fault tree, and we use possibility‐probability (probability‐possibility) transformations for propagating the epistemic uncertainty within purely probabilistic and possibilistic settings. The results of the different approaches (hybrid, probabilistic, and possibilistic) are compared with respect to the representation of uncertainty about the top event (limiting relative frequency) probability. Both the rationale underpinning the approaches and the computational efforts they require are critically examined. We conclude that the approaches relevant in a given setting depend on the purpose of the risk analysis, and that further research is required to make the possibilistic approaches operational in a risk analysis context.     

Probabilistic Risk Analysis and Terrorism Risk

Since the terrorist attacks of September 11, 2001, and the subsequent establishment of the U.S. Department of Homeland Security (DHS), considerable efforts have been made to estimate the risks of terrorism and the cost effectiveness of security policies to reduce these risks. DHS, industry, and the academic risk analysis communities have all invested heavily in the development of tools and approaches that can assist decisionmakers in effectively allocating limited resources across the vast array of potential investments that could mitigate risks from terrorism and other threats to the homeland. Decisionmakers demand models, analyses, and decision support that are useful for this task and based on the state of the art. Since terrorism risk analysis is new, no single method is likely to meet this challenge. In this article we explore a number of existing and potential approaches for terrorism risk analysis, focusing particularly on recent discussions regarding the applicability of probabilistic and decision analytic approaches to bioterrorism risks and the Bioterrorism Risk Assessment methodology used by the DHS and criticized by the National Academies and others.     

基于组合分类的消费者信用评估

为了降低单个分类模型的不稳定性,提高其应用于消费者信用评估的准确性,提出一种基于组合分类的消费者信用评估方法.首先通过有监督聚类将各个类别的数据样本划分为若干子集合,使得各个子集合中数据样本均来自同一类别,再对不同类别子集合之间进行两两组合得到训练样本子集合,然后在各个训练样本子集合中分别建立不同的分类模型.在分类模型结果综合阶段,以各个分类模型在待分类样本的近邻训练样本中的分类性能作为权重,对各个分类模型的结果进行加权投票以产生待分类样本的最终分类结果.实证研究以决策树作为基本分类器,通过在实际的消费者信用数据集上的比较分析,说明所提出方法相对于其它组合分类方法具有更高的分类准确性,可以有效应用于消费者信用评估.     

基于可变精度粗糙集的新兴技术企业信用风险识别

针对新兴技术企业信用风险评估的必要性和现有评估方法仅局限于财务指标且指标之间高度相关的缺点,借鉴了可变精度粗糙集（VPRS）模型具有噪声数据的强适应能力和强抗干扰能力的优点,提出了一类基于VPRS的新兴技术企业信用风险识别方法,并用已上市的部分新兴技术企业对其进行实证检验,检验结果表明了该方法具有较好的识别能力。该方法首先运用VPRS理论的最新研究成果,并借助于粗糙集分析软件ROSETYA,对由训练样本组成的数据关系表进行数据补缺、离散化及属性的β约简等处理,从而导出识别规则,形成识别规则库;然后集成二叉树构建一类新兴技术企业信用风险识别方法;最后用测试样本对方法的识别精度进行检验。     

文学中神圣植物意象的比较研究

在世界文学中，存在着一类非常古老、重复出现而又形态多样的文学原型——神圣植物意象，它的代表性原型是“宇宙树”。之所以谓之神圣，乃是因为它们常常反映出对人类而言极为重要的意义，如宇宙的创造,世界的结构,人类的诞生,生命的由来，甚至精神和文化的价值等。这些原初的文学意象，具有“原型”的典型特征，表现出远超出文学作品本身的深刻内涵。     

茶氨酸在茶树体内的分布规律和年变化的研究

从初春到秋天，连续采集茶园茶树与野生茶树的须根、主侧根、老茎、新茎、成叶、一芽二叶的新梢进行茶氨酸含量的测定．得出一年中含量规律为春季最高，以后逐渐下降，至秋季又略有回升．茶树中茶氨酸含量则是新梢、须根最高，成叶、新茎次之，而老茎与主侧根最低．同时也说明了施肥对茶树质量的影响．     

Using Incident Response Trees as a Tool for Risk Management of Online Financial Services

The article introduces the use of probabilistic risk assessment for modeling the incident response process of online financial services. The main contribution is the creation of incident response trees, using event tree analysis, which provides us with a visual tool and a systematic way to estimate the probability of a successful incident response process against the currently known risk landscape, making it possible to measure the balance between front‐end and back‐end security measures. The model is presented using an illustrative example, and is then applied to the incident response process of a Swedish bank. Access to relevant data is verified and the applicability and usability of the proposed model is verified using one year of historical data. Potential advantages and possible shortcomings are discussed, referring to both the design phase and the operational phase, and future work is presented.     

Hierarchical priors for Bayesian CART shrinkage

The Bayesian CART (classification and regression tree) approach proposed by Chipman, George and McCulloch (1998) entails putting a prior distribution on the set of all CART models and then using stochastic search to select a model. The main thrust of this paper is to propose a new class of hierarchical priors which enhance the potential of this Bayesian approach. These priors indicate a preference for smooth local mean structure, resulting in tree models which shrink predictions from adjacent terminal node towards each other. Past methods for tree shrinkage have searched for trees without shrinking, and applied shrinkage to the identified tree only after the search. By using hierarchical priors in the stochastic search, the proposed method searches for shrunk trees that fit well and improves the tree through shrinkage of predictions.