多状态系统的LAD决策树的建树方法研究

LAD( Logistic Data Analysis Tree)是一种逻辑数据分析技术 ,它将布尔代数和优化分析方法引入到了判别分析领域 ,提出了一种布尔变量集合的变量筛选和建模方法 ,并可以对冗余模式进行可视化识别与删除 .但目前的 LAD技术还仅限于二状态 ,而且算法复杂 .本文将 LAD决策树推广到了多状态情形 ,以三状态下建立 LAD决策树为例 ,提出了不可分辨度的定义 ,并以其下降最大作为寻找最优决策树的依据 .说明多状态下建立 LAD决策树的计算方法及重要的算法步骤 .最后 ,本文以鄱阳湖地区洪涝灾害影响研究为案例 ,采用 LAD决策树方法对其进行判别分析 .     

基于加总和乘积的方差分解法及其应用

研究了加总式和乘积式的方差分解问题,证明了在因变量等于各自变量之和的条件下,因变量方差等于各自变量与因变量的协方差之和;在因变量等于各自变量之乘积的条件下,因变量对数值的方差等于各自变量对数值与因变量对数值的协方差之和．以中国31个省份2005－2012年的居民人均收入及其影响因素的统计数据资料为例,说明了加总式和乘积式的方差分解法的具体应用．     

基于迭代自适应随机决策树的个人信用评估模型研究

信用评估是商业银行控制和防范信贷风险的关键途径.决策树模型较好的直观解释性使其成为当前个人信用评估中的常用模型,但决策树模型存在容易导致过拟合且预测精度有限的问题.通过在决策树模型算法中引入类随机森林随机有放回的抽样模式,运用机器自动循环迭代寻求最优树的建模思想,建立了自适应最优C5.0决策树个人信用评估模型.该模型具有快速收敛特征变量、较好的泛化能力和高预测精度的特点,在实证分析中对商业银行个人信用评估模型质量提升带来比较明显的改进效果.     

基于CHAID决策树的个人收入分析

本文提出了应用CHAID决策树方法来分析国民个人收入。首先本文全面分析了CHAID决策树的构造过程,最后通过实证分析,从大量的个人信息数据集中,运用CHAID决策树构建出了一个分析模型,该模型提供了许多潜在的、有用的信息。     

基于样条变换的PLS回归的非线性结构分析

基于样条变换的:PLS非线性回归模型既吸取了样条函数分段拟合以适应任意曲线连续变化的优点,又借鉴了偏最小二乘回归方法能够有效解决自变量集合高度相关的技术.针对多元加法模型,从理论和仿真试验的角度分别验证了,对于多个独立自变量对单因变量为非线性关系的数据系统,基于样条变换的PLS回归方法不仅能够有效实现自变量对因变量的整体预测,而且能够提取各维自变量对因变量的单独非线性作用特征,从而确定数据系统内部的复杂非线性结构关系,增强了模型的可解释性.     

求函数自变量取值范围的方法

<正>求函数自变量的取值范围,是函数概念中的一个重要知识点,我总结了初中数学求函数自变量取值范围的方法,供学生学习时参考.一、不同类型的函数关系式中自变量取值范围的求解方法.1.整式型:等式右边是关于自变量的整式,其自变量的取值范围是全体实数.     

由arctgx arcctgx=π/2所想到的

反余切的自变量相等时,它们的和等于常数π/2。由此可想到: 1。当反正切与反余切的和等于常数π/2时,它们的自变量是否相等? 2。当反正切的自变量与反余切的自变量     

基于残差平方和的线性模型选择简单方法及其相合性研究

本文研究了回归分析理论中的自变量选择的重要问题,利用线性回归模型自变量增减的残差平方和理论,获得了一种选择自变量的准则,该准则的分析表明,它计算简单,而且在一定的条件下具有强相合性.     

基法逐步回归

本文对于回归中自变量的选择提出了一个新方法，这个方法是逐步回归的推广，假定自变量中有一组Ｋ个变量，如果因变量与组中每个自变量的相关系数都很大，而组中任意两个自变量之间的相关系数很小，则称这组Ｋ个自变量变一个基组。传统的逐步回归每次吸收或剔除一个自变量，而本文提出的方法则对每个可能Ｋ元基组考虑吸收或剔除。本文通过一个实例说明，与传统的逐步回归方法相比，新方法更为灵活并能得到更好的结果。     

探讨一次函数应用题

不少与实际生活和生产有关的最大和最小值的应用题,我们可通过建立一次函数式y=kx+b(k≠0),利用函数的增减性求解:当k<0时,一次函数是减函数,在自变量x的取值范围内,由自变量x的最大值可求得y的最小值,由自变量x的最小值可求得y的最大值;当k>0时,一次函数是增函数,在自变量x的取值范围内,由自变量x的最大     

Predicting consumer preference for fast-food franchises: a data mining approach

The objectives of the study reported in this paper are: (1) to evaluate the adequacy of two data mining techniques, decision tree and neural network in analysing consumer preference for a fast-food franchise and (2) to examine the sufficiency of the criteria selected in understanding this preference. We build decision tree and neural network models to fit data samples collected from 800 respondents in Taiwan to understand the factors that determine their brand preference. Classification rules are generated from these models to differentiate between consumers who prefer the brand and those who do not. The generated rules show that while both decision tree and neural network models can achieve predictive accuracy of more than 80% on the training data samples and more that 70% on the cross-validation data samples, the neural network models compare very favourably to a decision tree model in rule complexity and the numbers of relevant input attributes.     

A survey of fuzzy decision tree classifier

Decision-tree algorithm provides one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Over the years, additional methodologies have been investigated and proposed to deal with continuous or multi-valued data, and with missing or noisy features. Recently, with the growing popularity of fuzzy representation, some researchers have proposed to utilize fuzzy representation in decision trees to deal with similar situations. This paper presents a survey of current methods for Fuzzy Decision Tree (FDT) designment and the various existing issues. After considering potential advantages of FDT classifiers over traditional decision tree classifiers, we discuss the subjects of FDT including attribute selection criteria, inference for decision assignment and stopping criteria. To be best of our knowledge, this is the first overview of fuzzy decision tree classifier.     

Analysis of the generalization ability of a full decision tree

Classification algorithms based on full decision trees are investigated. Due to the decision tree construction under consideration, all the features satisfying a branching criterion are taken into account at each special vertex. The generalization ability of a full decision tree is estimated by applying margin theory. It is shown on real-life problems that the construction of a full decision tree leads to an increase in the margins of the learning objects; moreover, the number of objects with a positive margin increases as well. It is shown that the empirical Rademacher complexity of a full decision tree is lower than that of a classical decision tree.     

Reusable components in decision tree induction algorithms

We propose a generic decision tree framework that supports reusable components design. The proposed generic decision tree framework consists of several sub-problems which were recognized by analyzing well-known decision tree induction algorithms, namely ID3, C4.5, CART, CHAID, QUEST, GUIDE, CRUISE, and CTREE. We identified reusable components in these algorithms as well as in several of their partial improvements that can be used as solutions for sub-problems in the generic decision tree framework. The identified components can now be used outside the algorithm they originate from. Combining reusable components allows the replication of original algorithms, their modification but also the creation of new decision tree induction algorithms. Every original algorithm can outperform other algorithms under specific conditions but can also perform poorly when these conditions change. Reusable components allow exchanging of solutions from various algorithms and fast design of new algorithms. We offer a generic framework for component-based algorithms design that enhances understanding, testing and usability of decision tree algorithm parts.     

Research on estimation model of the battery state of charge in a hybrid electric vehicle based on the classification and regression tree

ABSTRACT

In order to achieve the accurate estimation of state of charge (SOC) of the battery in a hybrid electric vehicle (HEV), this paper proposed a new estimation model based on the classification and regression tree (CART) which belongs to a kind of decision tree. The basic principle and modelling process of the CART decision tree were introduced in detail in this paper, and we used the voltage, current, and temperature of the battery in an HEV to estimate the value of SOC under the driving cycle. Meanwhile, we took the energy feedback of the HEV under the regenerative braking into consideration. The simulation data and experimental data were used to test the effectiveness of the estimation model of CART, and the results indicate that the proposed estimation model has high accuracy, the relative error of simulation is within 0.035, while the relative error of experiment is less than 0.05.     

Learning heuristics for basic block instruction scheduling

Instruction scheduling is an important step for improving the performance of object code produced by a compiler. A fundamental problem that arises in instruction scheduling is to find a minimum length schedule for a basic block—a straight-line sequence of code with a single entry point and a single exit point—subject to precedence, latency, and resource constraints. Solving the problem exactly is known to be difficult, and most compilers use a greedy list scheduling algorithm coupled with a heuristic. The heuristic is usually hand-crafted, a potentially time-consuming process. In contrast, we present a study on automatically learning good heuristics using techniques from machine learning. In our study, a recently proposed optimal basic block scheduler was used to generate the machine learning training data. A decision tree learning algorithm was then used to induce a simple heuristic from the training data. The automatically constructed decision tree heuristic was compared against a popular critical-path heuristic on the SPEC 2000 benchmarks. On this benchmark suite, the decision tree heuristic reduced the number of basic blocks that were not optimally scheduled by up to 55% compared to the critical-path heuristic, and gave improved performance guarantees in terms of the worst-case factor from optimality.     

Decision tree search methods in fuzzy modeling and classification

This paper proposes input selection methods for fuzzy modeling, which are based on decision tree search approaches. The branching decision at each node of the tree is made based on the accuracy of the model available at the node. We propose two different approaches of decision tree search algorithms: bottom-up and top-down and four different measures for selecting the most appropriate set of inputs at every branching node (or decision node). Both decision tree approaches are tested using real-world application examples. These methods are applied to fuzzy modeling of two different classification problems and to fuzzy modeling of two dynamic processes. The models accuracy of the four different examples are compared in terms of several performance measures. Moreover, the advantages and drawbacks of using bottom-up or top-down approaches are discussed.