Decision trees are particularly promising in symbolic representation and reasoning due to their comprehensible nature, which resembles the hierarchical process of human decision making. However, their drawbacks, caused by the single-tree structure, cannot be ignored. A rigid decision path may cause the majority class to overwhelm other class when dealing with imbalanced data sets, and pruning removes not only superfluous nodes, but also subtrees. The proposed learning algorithm, flexible hybrid decision forest (FHDF), mines information implicated in each instance to form logical rules on the basis of a chain rule of local mutual information, then forms different decision tree structures and decision forests later. The most credible decision path from the decision forest can be selected to make a prediction. Furthermore, functional dependencies (FDs), which are extracted from the whole data set based on association rule analysis, perform embeddedattribute selection to remove nodes rather than subtrees, thus helping to achieve different levels of knowledge representation and improve model comprehension in the framework of semi-supervised learning. Naive Bayes replaces the leaf nodes at the bottom of the tree hierarchy, where the conditional independence assumption may hold. This techniquereduces the potential for overfitting and overtraining and improves the prediction quality and generalization. Experimental results on UCI data sets demonstrate the efficacy of the proposed approach.

使用常规植被指数很难从卫星图像中准确识别和提取水体和水下植被，因为对水的强烈吸收会削弱浅水湖泊中水下植被反射的高近红外光谱特性。 这项研究以中国半干旱地区的乌兰苏海浅湖为研究地点，并提出了一种新的凹凸决策功能，可以利用高粉1号（GF-1）检测水下水生植物（SAV）和识别水体。 ），并于2015年7月和2015年8月获取分辨率为16米的多光谱卫星图像。同时，通过决策树方法对新兴植被，“黄台藻开花”和SAV进行了同时分类。 经过实地调查和核实，7月和8月的分类精度分别为92.17％和91.79％，表明GF-1数据重访期短至四天且空间分辨率高，可以满足水生植被要求的精度标准。萃取。 结果表明，凹凸决策函数在区分水和SAV方面优于传统的分类方法，从而大大提高了SAV的分类精度。 凹凸决策函数可以应用于在1.5 m透明度下SAV覆盖率大于0.3 m大于40％，SAV覆盖率大于0.1 m大于40％的水域，这可以为在其他区域准确提取SAV提供新的方法。

hd_knn_tree 使用RStudio对心脏病数据集进行决策树和K最近邻分析。 还要与进行比较，以找出哪种模型可以更好地预测数据集。 使用的技术/框架 Rstudio Rmarkdown 使用的RStudio库 库（caTools） 图书馆（班） 图书馆（kknn） 图书馆（插入符号） 图书馆（ROCR） 库（rpart） 库（rpart.plot） 图书馆（MASS） 图书馆（tidyverse） 图书馆（ggsci） 安装R软件包 rpack <- c("kknn", "caret", "class","caTools", "ROCR", "rpart", "rpart.plot", "MASS", "tidyverse", "ggsci") install.packages(rpack) 数据集 来自UCI的包含76个代表患者状况的属性。 本文的数据集来

TheSparksFoundation_Task-4_Decision-Trees 决策树是用于分析数据的图形表示。 决策树以这种方式为我们提供数据，我们可以轻松地读取，理解和分析数据。 决策树算法属于监督学习算法家族。 ...使用决策树的目的是创建一个训练模型，该模型可以通过学习从先前数据（训练数据）推断出的简单决策规则来预测目标变量的类或值

本文针对多输入多输出正交频分复用（MIMOOFDM）系统，提出了一种迭代决策导向信道估计算法。 该算法分为两部分：信道预测和信道估计。 信道预测的基本思想是使用自回归模型和信道的先验信息来预测信道状态。 然后，通过使用信道预测信息和接收信号来估计信道状态。 仿真结果表明，该方法可以提高信道估计的准确性，提高MIMO-OFDM系统的性能。 与传统的DDCE方法相比，当SNR为30时，迭代DD-CE方法的BER提升了近10％，估计精度提高了近2dB。

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Modeling the Decision Process of Optimal Travel Route based on Analytic Hierarchy Process

This paper presents a Crotch Ensemble classification model for high dimensional data. A Crotch Ensemble is obtained from a decision cluster tree built by calling a clustering algorithm recursively. A crotch is an inner node of the tree together with its direct children. If the children of a crotch h

Runtime Models Based on Dynamic Decision Networks: Enhancing the Decision-making in the Domain of Ambient Assisted Living Applications

斯坦福大学2021年1月，Mykel J. Kochenderfer教授主编。Board introduction to algorithms for optimal decision making under uncertainty. We cover a wide variety of topics related to decision making, introducing the underlying mathematical problem formulations and the algorithms for solving them.