正态判别中完全贝叶斯和贝叶斯最小二乘准则的比较

本文研究了两个具有相同协方差阵的多元正态总体在自然共轭先验分布下的判别分析问题，证明了当可观察变量的个数趋无穷时，贝叶斯判别函数与一基于样本的判别函数渐近等阶。     

基于贝叶斯逐步判别法的入侵检测模型研究

基于贝叶斯逐步判别法构建入侵检测模型,将入侵检测转化为一个分类判别问题,基于步进式引入的方法淘汰冗余的特征变量,能够在保障判别效果的前提下有效降低原分类判别函数的计算复杂度.使用KDD CUP99数据中10%数据集作为实验数据,以常见的拒绝服务攻击(DoS攻击)为例创建具体的模型实例,实验结果表明,模型对于样本内连接记录的回代判对率和样本外连接记录的检测正确率均较高.     

利用Kullback散度的多组二次判别分析

本文介绍用两个线性变换将 m 维因子向量进行变换,建立二次判别函数的方法.这种变换的优点在于可用 Kullback 散度指标对二次判别函数进行组合和优选.大量计算实例表明,这种方法的效果优于其它判别分析方法.     

优选法在自动聚集中的应用

调焦判别函数随焦距分布的曲线是一个单峰曲线 ,因此我们可以运用优选法来确定最佳像面的位置 .     

判别分析法对增发股投资价值的分析及预测

本文通过增发股相关指标数据的特别处理 ,对增发股投资价值进行了判别分析 ,求得了判别函数 ,并应用于增发股的投资预测     

二阶连续混合偏导数相等的一个简洁证明

本文运用导数判别函数单调性的知识,通过构造函数给出了二阶连续混合偏导数相等的一个证明,比数学分析中的证明方法简易.     

判别分析在小儿支原体肺炎早期诊断中的应用

本文对辽宁中医学院附属医院儿科收治的 90例肺炎病例进行了判别分析 ,求得判别函数 ,并应用于临床 ,收到了较为满意的效果。     

一道例题的若干应用

基于一道关于单侧导数及单侧极值的例题,给出若干微分命题的证明,并应用这道例题获得一个判别函数极值的方法.     

用导数判别函数的一致连续性

本文主要给出了用导数判别函数在一般区间上一致连续的方法,并举例说明不可以建立关于一致连续的比较判别法．     

逐步判别有关结论及其证明

本文给出了在建立逐步判别函数估计式时检验附加信息统计量的计算公式 ,并证明了在检验所引入或剔除变量的判别能力和显著性的有关结论 .     

An additive utility mixed integer programming model for nonlinear discriminant analysis

Mathematical programming (MP) discriminant analysis models can be used to develop classification models for assigning observations of unknown class membership to one of a number of specified classes using values of a set of features associated with each observation. Since most MP discriminant analysis models generate linear discriminant functions, these MP models are generally used to develop linear classification models. Nonlinear classifiers may, however, have better classification performance than linear classifiers. In this paper, a mixed integer programming model is developed to generate nonlinear discriminant functions composed of monotone piecewise-linear marginal utility functions for each feature and the cut-off value for class membership. It is also shown that this model can be extended for feature selection. The performance of this new MP model for two-group discriminant analysis is compared with statistical discriminant analysis and other MP discriminant analysis models using a real problem and a number of simulated problem sets.     

改进的基于特征向量集的距离判别及在企业信用评价中的应用

在基于特征向量集的距离判别的基础上,提出新的判别分析方法,试图解决现有判别分析方法中计算量大及对复杂数据判别效果差的缺点.同时,将方法用于企业信用评价中,并与传统的判别方法及一些改进的判别方法作比较,实验结果表明,方法提高了企业信用评价的准确率.     

基于R软件的Bayes判别分析在医学中的应用

Bayes判别在进行判别分析时考虑到各总体出现的先验概率、预报的先验概率及错判造成的损失,其判别效能优于其他判别方法.对Bayes判别方法详细介绍的基础上,利用R软件对一组舒张压和胆固醇数据分别进行Bayes判别分析、Fisher判别分析和基于距离的判别分析,对比三种不同方法下得到的判别结果,结果表明Bayes判别分析得到的分类结果精度较高,Bayes判别分析在医学领域有较好的应用前景.     

On improved estimation of normal precision matrix and discriminant coefficients

The problem of estimating the precision matrix of a multivariate normal distribution model is considered with respect to a quadratic loss function. A number of covariance estimators originally intended for a variety of loss functions are adapted so as to obtain alternative estimators of the precision matrix. It is shown that the alternative estimators have analytically smaller risks than the unbiased estimator of the precision matrix. Through numerical studies of risk values, it is shown that the new estimators have substantial reduction in risk. In addition, we consider the problem of the estimation of discriminant coefficients, which arises in linear discriminant analysis when Fisher's linear discriminant function is viewed as the posterior log-odds under the assumption that two classes differ in mean but have a common covariance matrix. The above method is also adapted for this problem in order to obtain improved estimators of the discriminant coefficients under the quadratic loss function. Furthermore, a numerical study is undertaken to compare the properties of a collection of alternatives to the “unbiased” estimator of the discriminant coefficients.     

Feature Extraction for Nonparametric Discriminant Analysis

In high-dimensional classification problems, one is often interested in finding a few important discriminant directions in order to reduce the dimensionality. Fisher's linear discriminant analysis (LDA) is a commonly used method. Although LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. Using a likelihood-based interpretation of Fisher's LDA criterion, we develop a general method for finding important discriminant directions without assuming the class densities belong to any particular parametric family. We also show that our method can be easily integrated with projection pursuit density estimation to produce a powerful procedure for (reduced-rank) nonparametric discriminant analysis.     

Penalized multinomial mixture logit model

Normal distribution based discriminant methods have been used for the classification of new entities into different groups based on a discriminant rule constructed from the learning set. In practice if the groups are not homogeneous, then mixture discriminant analysis of Hastie and Tibshirani (J R Stat Soc Ser B 58(1):155–176, 1996) is a useful approach, assuming that the distribution of the feature vectors is a mixture of multivariate normals. In this paper a new logistic regression model for heterogenous group structure of the learning set is proposed based on penalized multinomial mixture logit models. This approach is shown through simulation studies to be more effective. The results were compared with the standard mixture discriminant analysis approach using the probability of misclassification criterion. This comparison showed a slight reduction in the average probability of misclassification using this penalized multinomial mixture logit model as compared to the classical discriminant rules. It also showed better results when applied to practical life data problems producing smaller errors.     

Integer programming methods for normalisation and variable selection in mathematical programming discriminant analysis models

Mathematical programming discriminant analysis models must be normalised to prevent the generation of discriminant functions in which the variable coefficients and the constant term are zero. This normalisation requirement can cause difficulties, and unlike statistical discriminant analysis, variables cannot be selected in a computationally efficient way with mathematical programming discriminant analysis models. Two new integer programming normalisations are proposed in this paper. In the first, binary variables are used to represent the constant term, but with this normalisation functions with a zero constant term cannot be generated and the variable coefficients are not invariant under origin shifts. These limitations are overcome by using integer programming methods to constrain the sum of the absolute values of the variable coefficients to a constant. These new normalisations are extended to allow variable selection with mathematical programming discriminant analysis models. The use of these new applications of integer programming is illustrated using published data.     

Classification Trees With Bivariate Linear Discriminant Node Models

This article introduces a classification tree algorithm that can simultaneously reduce tree size, improve class prediction, and enhance data visualization. We accomplish this by fitting a bivariate linear discriminant model to the data in each node. Standard algorithms can produce fairly large tree structures because they employ a very simple node model, wherein the entire partition associated with a node is assigned to one class. We reduce the size of our trees by letting the discriminant models share part of the data complexity. Being themselves classifiers, the discriminant models can also help to improve prediction accuracy. Finally, because the discriminant models use only two predictor variables at a time, their effects are easily visualized by means of two-dimensional plots. Our algorithm does not simply fit discriminant models to the terminal nodes of a pruned tree, as this does not reduce the size of the tree. Instead, discriminant modeling is carried out in all phases of tree growth and the misclassification costs of the node models are explicitly used to prune the tree. Our algorithm is also distinct from the “linear combination split” algorithms that partition the data space with arbitrarily oriented hyperplanes. We use axis-orthogonal splits to preserve the interpretability of the tree structures. An extensive empirical study with real datasets shows that, in general, our algorithm has better prediction power than many other tree or nontree algorithms.     

The entropic discriminant

The entropic discriminant is a non-negative polynomial associated to a matrix. It arises in contexts ranging from statistics and linear programming to singularity theory and algebraic geometry. It describes the complex branch locus of the polar map of a real hyperplane arrangement, and it vanishes when the equations defining the analytic center of a linear program have a complex double root. We study the geometry of the entropic discriminant, and we express its degree in terms of the characteristic polynomial of the underlying matroid. Singularities of reciprocal linear spaces play a key role. In the corank-one case, the entropic discriminant admits a sum of squares representation derived from the discriminant of a characteristic polynomial of a symmetric matrix.     

Adapting the Warmack–Gonzalez algorithm to handle discrete data

Soltysik and Yarnold propose, as a method for two-group multivariate optimal discriminant analysis (MultiODA), selecting a linear discriminant function based on an algorithm by Warmack and Gonzalez. An important assumption underlying the Warmack–Gonzalez algorithm is likely to be violated when the data in the discriminant training samples are discrete, and in particular when they are nominal, causing the algorithm to fail. We offer modest changes to the algorithm that overcome this limitation.