共查询到17条相似文献,搜索用时 62 毫秒
1.
2.
考虑航空交通网络状态特征对航班延误的影响,将上游的航班延误状态特征加入到预测因素中,并使用梯度提升决策树(Gradient Boost Decision Tree,GBDT)的方法构建了航班延误预测模型.与以往的决策树算法、SVM分类算法、RF算法相比,GBDT算法在航班延误分类预测上具有更高的准确度,可有效提高机场运行管理效率. 相似文献
3.
4.
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。 相似文献
5.
6.
7.
基于变精度加权平均粗糙度决策树的财务预警研究 总被引:2,自引:0,他引:2
运用聚类方法把公司财务状况分为5个等级,分别为财务状况健康,良好,一般,预警和危机,与以往将研究样本分为ST和非ST两类的财务预警模型相比,5分类模型更加精确合理,贴近实际。同时基于指标相关性和指标重要度对33个财务指标进行了约简,得到9个能够反映企业财务状况的财务指标。以约简后的9个指标及5个等级的财务状况来建立决策树,指标体系和财务等级更加合理。树的生成过程运用粗糙集中的变精度加权平均粗糙度作为选择测试属性的方法,每次选择变精度加权平均粗糙度值最小的属性作为分支结点。变精度加权平均粗糙度的应用提高了决策树的防噪声能力,复杂性较低且能有效提高分类效果。实证研究表明将它应用到财务预警领域,提高了财务预警的分类精度。 相似文献
8.
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 . 相似文献
9.
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. 相似文献
10.
11.
12.
In economic decision problems such as credit loan approval or risk analysis, models are required to be monotone with respect to the decision variables involved. Also in hedonic price models it is natural to impose monotonicity constraints on the price rule or function. If a model is obtained by a unbiased search through the data, it mostly does not have this property even if the underlying database is monotone. In this paper, we present methods to enforce monotonicity of decision trees for price prediction. Measures for the degree of monotonicity of data are defined and an algorithm is constructed to make non-monotone data sets monotone. It is shown that monotone data truncated with noise can be restored almost to the original data by applying this algorithm. Furthermore, we demonstrate in a case study on house prices that monotone decision trees derived from cleaned data have significantly smaller prediction errors than trees generated using raw data.MSC code:
90-08 (Computational methods) 相似文献
13.
《Journal of computational and graphical statistics》2013,22(3):512-530
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. 相似文献
14.
A major task in service management is the timely and cost efficient provision of spare parts for durable products. This especially
holds good, when the regular production of the product, its components and parts has been discontinued, but customer service
still has to be guaranteed for quite a long time. In such post product life cycle period, three options are available to organize
the spare parts acquisition, namely (i) setting up a single large order within the final lot of regular production, (ii) performing
extra production runs until the end of service and (iii) using remanufacturing to gain spare parts from used products. These
three options are characterized by different cost and flexibility properties. Due to the time-variability and uncertainty
of demands for spare parts and also that of the returns of used products, it is a challenging task to find out the optimal
combination of these three options. In this paper we show how this problem can be modeled and solved by Decision Tree and
stochastic Dynamic Programming procedure. Based on the Dynamic Programming approach a heuristic method is proposed, which
can be employed to come up with a simple solution procedure for real-world spare parts acquisition problems during the post
product life cycle. A numerical example is presented to demonstrate the application of the solution methods described in the
paper. 相似文献
15.
《Journal of computational and graphical statistics》2013,22(1):206-218
Regression trees are a popular alternative to classical regression methods. A number of approaches exist for constructing regression trees. Most of these techniques, including CART, are sequential in nature and locally optimal at each node split, so the final tree solution found may not be the best tree overall. In addition, small changes in the training data often lead to large changes in the final result due to the relative instability of these greedy tree-growing algorithms. Ensemble techniques, such as random forests, attempt to take advantage of this instability by growing a forest of trees from the data and averaging their predictions. The predictive performance is improved, but the simplicity of a single-tree solution is lost.In earlier work, we introduced the Tree Analysis with Randomly Generated and Evolved Trees (TARGET) method for constructing classification trees via genetic algorithms. In this article, we extend the TARGET approach to regression trees. Simulated data and real world data are used to illustrate the TARGET process and compare its performance to CART, Bayesian CART, and random forests. The empirical results indicate that TARGET regression trees have better predictive performance than recursive partitioning methods, such as CART, and single-tree stochastic search methods, such as Bayesian CART. The predictive performance of TARGET is slightly worse than that of ensemble methods, such as random forests, but the TARGET solutions are far more interpretable. 相似文献
16.
The aim of this paper is to optimize the benchmarks and prioritize the variables of decision-making units (DMUs) in data envelopment analysis (DEA) model. In DEA, there is no scope to differentiate and identify threats for efficient DMUs from the inefficient set. Although benchmarks in DEA allow for identification of targets for improvement, it does not prioritize targets or prescribe level-wise improvement path for inefficient units. This paper presents a decision tree based DEA model to enhance the capability and flexibility of classical DEA. The approach is illustrated through its application to container port industry. The method proceeds by construction of multiple efficient frontiers to identify threats for efficient/inefficient DMUs, provide level-wise reference set for inefficient terminals and diagnose the factors that differentiate the performance of inefficient DMUs. It is followed by identification of significant attributes crucial for improvement in different performance levels. The application of this approach will enable decision makers to identify threats and opportunities facing their business and to improve inefficient units relative to their maximum capacity. In addition, it will help them to make intelligent investment on target factors that can improve their firms’ productivity. 相似文献
17.
物元评判模型及其在学习评价中的应用 总被引:2,自引:0,他引:2
对物元评判模型进行了分析,将它应用于学生学习评价,定义了新的关联度函数,分析表明模型是一类优良模型,能有效的运用于学生学习评价中。 相似文献