Parametric and non-parametric combination model to enhance overall performance on default prediction

The probability of default（PD） is the key element in the New Basel Capital Accord and the most essential factor to financial institutions＇ risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model（BLRM）,support vector machine（SVM）,and decision tree（DT） are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis（MDA） is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.     

Target recognition based on modified combination rule

1 .INTRODUCTIONThe evidence theory also known as D-S theory isan approach handling of uncertainty .It was firstput forward by Dempster A.P.in 1968 and thenmodified by his student G. Shafer in 1971[1 ~2].This theoryis the extension of Bayesiantheory andfurthermore it can deal with uncertainty caused bythe inaccurate or unknown of knowledge . So ,ithas been extremely wide used in data fusion field.However ,everything has its own weakness .It isinvalid when dealing with highly conflict e…     

Novel algorithm for constructing support vector machine regression ensemble

1 .INTRODUCTIONRecently , support vector machine (SVM)[1]is anovel and promising technique in the fields of ma-chine learning and classification or regression pre-diction accompanying artificial neural network.InSVM,several learning algorithms can be obtainedgiven different inner-product functions named ker-nel functions ,such as polynomial approach,Bayes-ian classification、radial basic function method、multilayer perceptron network[2]. By now,it hasbeen successfully applied in many ar…     

基于图像特征的卷积核初始化方法

针对当前卷积核初始化方法易导致网络不稳定及主成分分析算法对网络结构限制的问题, 提出一种基于图像特征的卷积核初始化方法. 该方法先结合模糊处理技术和边缘处理技术对图像进行采样, 再将采样后的数据随机分组, 使用主成分分析算法提取各组数据的主成分, 初始化卷积核. 将该方法应用于数据集Cifar-10和Corel-1000, 并与Gauss初始化方法和He初始化方法进行对比测试, 实验结果表明, 该方法性能优于其他卷积核初始化方法.     

表贴式永磁同步电机解析建模与极槽配合选取

合理选取极槽配合是电机设计的关键环节,为解决传统有限元技术重复建模带来的耗时和资源占用等问题,建立了永磁同步电机解析模型,来分析不同极槽配合对电机性能的影响.在二维极坐标系,将电机划分成永磁体、气隙、电枢槽和槽开口四类子域,并构建了各子域的拉普拉斯方程或泊松方程,利用分离变量法结合边界条件对各子域进行解析计算,完成电机解析模型的搭建.借助有限元结果验证了该解析模型的正确性,并采用该解析模型研究了不同极槽配合对电机性能的影响.通过对比发现,60槽8极电机的齿槽转矩远小于其他几种,转矩输出特性更好;分数槽配合的负载径向气隙磁密正弦性比整数槽更好,能有效地降低转矩脉动;在一定范围内,多槽结构有利于提升电机的转矩特性.     

多级组合发动机混合动力电动汽车燃油经济性的数值模拟

提出了多级组合发动机的概念,并将其作为混合动力电动汽车的动力。与常规混合动力电动汽车相比,在欧洲NEDC、美国HWFET和日本1015汽车循环中,最高燃油经济性分别提高8.09%、9.69%和3.5%。在不同的电池组初始充电状态和不同的多级组合发动机功率比的情况下,对其进行了燃油经济性和发动机平均效率的模拟。模拟结果表明:考虑结构设计的复杂性和经济性,与传统动力相比,以等功率比的双级组合发动机作为混合动力电动汽车动力更为合理,更具可行性。     

直觉模糊框架内的区间证据组合方法研究

区间证据的组合是区间证据理论中的核心问题之一,尽管相关学者试图将D-S证据理论中的证据组合规则进行推广以实现区间证据的组合,但该问题仍未得到完全解决.目前已有的组合方法都是基于优化模型来确定融合结果中各基本概率质量(BPM)的上下界,不仅算法复杂度较高,而且这类方法不满足结合律,对多个区间证据进行组合时不能依次对各个证据进行组合,难以满足时域信息融合的需求.本文基于直觉模糊集与证据理论之间的关系,在直觉模糊框架内对区间证据的组合问题进行研究.首先基于证据理论定义一种新的直觉模糊运算,通过区间证据与直觉模糊集之间的相互转换,提出一种基于直觉模糊集的区间证据组合方法,该方法满足交换律和结合律,对多个区间证据进行组合时具有较强的灵活性.数值仿真表明,基于直觉模糊集的区间证据组合方法可以获得合理的组合结果,而且算法复杂度远低于现有方法.     

人文关怀视角下的体教结合新思考

体教结合是竞技体育可持续发展的必由之路,可是在实施过程中却没有把握好发展的主线,导致在20多年后仍然处在探索阶段,没有发挥所期望的效果.以人文关怀为视角,重新思考这一命题,认为体教结合是竞技体育发展的必然,竞技体育的发展必须以运动员的全面发展为前提,通过剖析当前体教结合的困境,提出体教结合模式改革的具体思路.     

NaOH预处理稻草秸秆的软化效果研究

采用NaOH预处理稻草秸秆,设计正交试验研究了与稻草秸秆质量损失率有关的3个影响因子,并在此基础上通过组合试验进一步研究了稻草秸秆质量损失率与NaOH溶液质量分数、浸泡时间的关系.此外,首次引入成本评价指标(综合成本指数),选出最优试验组合.结果表明:各因子影响强弱顺序依次为浸泡时间、 NaOH溶液质量分数、处理温度,处理温度的影响很小,在常温下预处理比较经济;质量损失率跟浸泡时间的关系曲线变化明显,而跟NaOH溶液质量分数的关系曲线变化波动不大;最优试验组合是NaOH溶液质量分数为1%,稻草秸秆浸泡时间为6 h(常温),在此条件下,稻草秸秆的软化效果良好,有利于制砖粘结成型,且综合成本较低.     

Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices

Improving the prediction accuracy of agricultural product futures prices is important for investors, agricultural producers, and policymakers. This is to evade risks and enable government departments to formulate appropriate agricultural regulations and policies. This study employs the ensemble empirical mode decomposition (EEMD) technique to decompose six different categories of agricultural futures prices. Subsequently, three models—support vector machine (SVM), neural network (NN), and autoregressive integrated moving average (ARIMA)—are used to predict the decomposition components. The final hybrid model is then constructed by comparing the prediction performance of the decomposition components. The predicting performance of the combination model is then compared with the benchmark individual models: SVM, NN, and ARIMA. Our main interest in this study is on short-term forecasting, and thus we only consider 1-day and 3-day forecast horizons. The results indicate that the prediction performance of the EEMD combined model is better than that of individual models, especially for the 3-day forecasting horizon. The study also concluded that the machine learning methods outperform the statistical methods in forecasting high-frequency volatile components. However, there is no obvious difference between individual models in predicting low-frequency components.