一种基于决策森林的单调分类方法

单调分类问题是特征与类别之间带有单调性约束的有序分类问题.对于符号数据的单调分类问题已有较好的方法,但对于数值数据,现有的方法分类精度和运行效率有限.提出一种基于决策森林的单调分类方法(monotonic classification method based on decision forest, MCDF),设计采样策略来构造决策树,可以保持数据子集与原数据集分布一致,并通过样本权重避免非单调数据的影响,在保持较高分类精度的同时有效提高了运行效率,同时这种策略可以自动确定决策森林中决策树的个数.在决策森林进行分类时,给出了决策冲突时的解决方法.提出的方法既可以处理符号数据,也可以处理数值数据.在人造数据集、UCI及真实数据集上的实验数据表明:该方法可以提高单调分类性能和运行效率,缩短分类规则的长度,解决数据集规模较大的单调分类问题.     

Sensitivity Analysis for Decision Boundaries

A novel approach is presented to visualize and analyze decision boundaries for feedforward neural networks. First order sensitivity analysis of the neural network output function with respect to input perturbations is used to visualize the position of decision boundaries over input space. Similarly, sensitivity analysis of each hidden unit activation function reveals which boundary is implemented by which hidden unit. The paper shows how these sensitivity analysis models can be used to better understand the data being modelled, and to visually identify irrelevant input and hidden units.     

一种多层次抽象语义决策图像分类方法

视觉词包(Bag-of-visual-words, BoVW) 模型是一种有效的图像分类方法. 本文提出一种基于语义抽象的多层次决策(Multiple layer decision, MLD) 方法,通过在BoVW 中引入抽象语义进行多层次扩展,采用语义保留方法生成具有语义的视觉词典,利用自底向上的方式逐层传递语义, 训练上层语义分类器;分类时采用自顶向下方式逐层判断待测样本的类别. 用标准数据集验证方法的分类性能. 结果表明,本文提出的方法与主流分类方法相比具有更好的分类性能.     

决策域分布保持的启发式属性约简方法

在决策粗糙集中,由于引入了概率阈值,属性增加或减少时,正域或者非负域有可能变大、变小或者不变,即属性的增减与决策域（正域或非负域）之间不再具有单调性.分析结果表明,现有的基于整个决策域的属性约简定义可能会改变决策域.为使决策域保持不变,引入了正域分布保持约简与非负域分布保持约简的概念.此外,决策域的非单调性使得属性约简算法必须检查一个属性集合的所有子集.为了简化算法设计,提出了正域和非负域分布条件信息量的定义,并证明其满足单调性,从而为设计决策域分布保持约简的启发式计算方法提供了理论基础.为了进一步获得最小约简,提出一种基于遗传算法的决策域分布保持启发式约简算法,并在两种单调的决策域分布条件信息量基础上构造了新算子,即修正算子,确保遗传算法找到的是约简而不是约简的超集.对比实验从分类正确率与误分类代价两个方面都反映了决策域分布保持约简定义的合理性,并且,所提出的遗传算法在大多数情况下都找到了最小约简.     

一种计算封闭区域周长和面积的新方法

区域面积和周长是图像分析与识别过程中所需的两个重要参数。本文提出了一种计算区域面积和周长的新方法,该方法的主要特点是算法简单,可借助快速傅立叶变换实现。文中详细地推导了有关计算公式,给出了快速算法的实现过程。实验表明,该方法具有较高的计算精度。由于可采用快速傅立叶变换算法为计算工具,该方法易于实现并具有很高的运算速度。     

基于凸包的人脸粗分类方法

针对大型人脸数据库中进行人脸匹配识别时,存在识别速度时间长、影响实时应用效果的问题。提出了一个基于凸包的人脸粗分类新方法。该方法从几何模式特征出发,以抽取人脸的二维凸包不变量特征为基础,使用层次聚类对人脸的轮廓线进行粗分类,建立人脸数据库的层次索引结构。在实验中,将MUCT和PICS人脸数据库的正面人脸图像粗分为六类,分类的平均准确率约为89%。验证了该方法在人脸数据库上执行快速粗分类是可行的。     

C-Net: A Method for Generating Non-deterministic and Dynamic Multivariate Decision Trees

Despite the fact that artificial neural networks (ANNs) are universal function approximators, their black box nature (that is, their lack of direct interpretability or expressive power) limits their utility. In contrast, univariate decision trees (UDTs) have expressive power, although usually they are not as accurate as ANNs. We propose an improvement, C-Net, for both the expressiveness of ANNs and the accuracy of UDTs by consolidating both technologies for generating multivariate decision trees (MDTs). In addition, we introduce a new concept, recurrent decision trees, where C-Net uses recurrent neural networks to generate an MDT with a recurrent feature. That is, a memory is associated with each node in the tree with a recursive condition which replaces the conventional linear one. Furthermore, we show empirically that, in our test cases, our proposed method achieves a balance of comprehensibility and accuracy intermediate between ANNs and UDTs. MDTs are found to be intermediate since they are more expressive than ANNs and more accurate than UDTs. Moreover, in all cases MDTs are more compact (i.e., smaller tree size) than UDTs. Received 27 January 2000 / Revised 30 May 2000 / Accepted in revised form 30 October 2000     

Inductive Learning Methods for Knowledge-Based Decision Support: A Comparative Analysis

This paper describes the inductive learning methods for generating decision rules in decision support systems. Three similarity-based learning systems are studied based on: (1) the AQ-Star method, (2) the Tree-Induction method, and (3) the Probabilistic Learning method. Loan evaluation examples and empirical data are used as a basis for comparing these inductive learning methods on their algorithmic characteristics and decision support performance.