网站营销咨询顾问,课程网站建设课程,一个空间两个网站对seo,企业网站建设公司宣武#x1f4a5;#x1f4a5;#x1f49e;#x1f49e;欢迎来到本博客❤️❤️#x1f4a5;#x1f4a5; #x1f3c6;博主优势#xff1a;#x1f31e;#x1f31e;#x1f31e;博客内容尽量做到思维缜密#xff0c;逻辑清晰#xff0c;为了方便读者。 ⛳️座右铭欢迎来到本博客❤️❤️ 博主优势博客内容尽量做到思维缜密逻辑清晰为了方便读者。 ⛳️座右铭行百里者半于九十。 本文目录如下 目录 1 概述 2 运行结果 3 参考文献 4 Matlab代码实现 1 概述
我们开发了两种进化算法即合取子句进化算法CCEA和析取范式进化算法DNFEA旨在探索与真实世界数据中的复杂交互相关的因果关系。这些算法可以应用于监督学习任务帮助我们发现与特定目标结果比如疾病相关的复杂多变量关系。在不同类型的数据集中包括带有噪声、缺失数据和多种数据类型连续、有序和标称的情况下CCEA能够寻找特征上位之间的交互。为了防止过拟合特征交互CCEA还利用特征敏感度函数来辅助筛选。而DNFEA主要用于在CCEA的基础上寻找更强相关性的异构组合这些组合能够比任何单个连接子句更好地预测输出类别。CCEA和DNFEA都使用超几何概率质量函数作为适应度函数来评估。
总的来说我们提出了一种新的进化算法旨在从批量数据中发现复杂分类问题的因果关系规则。这种方法的关键特点包括a使用超几何概率质量函数作为评估适应度的统计指标以量化临时关联结果与目标类之间的偶然性概率同时考虑数据集大小、缺失数据和结果类别的分布情况b采用串联年龄分层进化算法演化出连接子句的简约档案以及这些连接子句的析取使得每个连接子句都与结果类之间具有概率显著关联c使用单独的档案箱来存储不同顺序的子句并具有动态调整的顺序特定阈值。我们通过在多个基准问题上的实验验证了该方法的有效性这些问题包括具有异质性、上位性、重叠、类别关联噪声、缺失数据、无关特征和类别不平衡等各种组合。此外我们还在更真实的合成基因组数据集上进行了验证该数据集具有异质性、上位性、外源特征和噪声。在所有合成上位基准问题中我们始终能够准确恢复出用于生成数据的真实因果关系规则集。最后我们还讨论了将这种方法应用于真实世界调查数据集的潜在应用该数据集旨在提供有关恰加斯病可能的生态健康干预措施的信息。
2 运行结果 部分代码
% set the number of address bits for the majority-on problem
NumFeat5; % set the number of observations
NumObs1250;% Now create the majority on dataset
Data(rand(NumObs,NumFeat)0.5)0;
% Determine output
Output(sum(Data,2)NumFeat/2)0;% There are three data types that can be input into the CCEA
% 1) continuous or ordinal data (ContData)
% 2) nominal data (Cat
% 3) binary data or any feature where the user only wants one value
% assigned to a feature in a conjunctive clause
% For each data type list the corresponding columns in the Data matrix that
% correspond to the data type of the feature (i.e., if the data in columns
% 1 and 3 are ordinal or continuous then ConOrdData[1 3]).;
ContOrdData[]; % To be used for ordinal or continuous features
NomData[]; % To be used for nominal features
BinData1:NumFeat; % To be used for binary features or any feature where % the user only wants one value associated with the% conjunctive clause.% Set the target class
TargetClassOutput1;% In this case only data with an output of 1 will be% analyzed% Run my algorithm convert the data to binary
[DataBin, Param, DataSum]Data2BinaryTarget(Data, Output, ...ContOrdData, NomData, BinData, TargetClass);%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Set the CCEA parameters
% The below settings are appropriate but not necessarily optimal for the
% 6-bit multiplexer dataset. The user can play with the parameter settings
% to find the best combination for a given dataset.
% Note: there are numerous input parameters for the CCEA. The idea is to
% give the user control over the optimal way to search a dataset. For
% instance, Datasets with binary features may require fewer age layers and
% fewer generations between novel generations; while datasets with
% continuous or ordinal features may require more age layers and more
% generations between novel generations.
Param.NumNewPopNumFeat; % The # of new offspring created every Param.GENn
Param.TotGens30; % Total # generations to run the CCEA
% Param.FeatLabels[]; % The feature labels (not needed for CCEA but % necessary for understanding the features)
Param.BestFitfalse(); % Will record the best hypergeometric fitness for % each CC order each generation
Param.ALna5; % The # of layers that are not archived % (helps maintain diversity)
Param.GENn3; % The # of generations until a new population of offspring % are created.
Param.NonArchLMaxParam.NumNewPop*1;% Max population per non-archive layer
Param.ArchOffParam.NonArchLMax*Param.ALna; %The max # of Archive offspring %created each generation
Param.Px0.5; % Probability of crossover
Param.Pwc0.75; % probability that feature selected for mutation will be % removed from the conjunctive clause
Param.Pm1/NumFeat; % probability that a feature will be selected for % mutation. Only if the parent is selected for mutation% instead of crossover.
Param.TournSize3; % # of parents with replacement that are in the % tournament to mate with the parent. Only most fit will % mate.
% set the number of address bits for the majority-on problem NumFeat5;
% set the number of observations NumObs1250;
% Now create the majority on dataset Data(rand(NumObs,NumFeat)0.5)0; % Determine output Output(sum(Data,2)NumFeat/2)0;
% There are three data types that can be input into the CCEA % 1) continuous or ordinal data (ContData) % 2) nominal data (Cat % 3) binary data or any feature where the user only wants one value % assigned to a feature in a conjunctive clause % For each data type list the corresponding columns in the Data matrix that % correspond to the data type of the feature (i.e., if the data in columns % 1 and 3 are ordinal or continuous then ConOrdData[1 3]).; ContOrdData[]; % To be used for ordinal or continuous features NomData[]; % To be used for nominal features BinData1:NumFeat; % To be used for binary features or any feature where % the user only wants one value associated with the % conjunctive clause.
% Set the target class TargetClassOutput1;% In this case only data with an output of 1 will be % analyzed
% Run my algorithm convert the data to binary [DataBin, Param, DataSum]Data2BinaryTarget(Data, Output, ... ContOrdData, NomData, BinData, TargetClass); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Set the CCEA parameters % The below settings are appropriate but not necessarily optimal for the % 6-bit multiplexer dataset. The user can play with the parameter settings % to find the best combination for a given dataset. % Note: there are numerous input parameters for the CCEA. The idea is to % give the user control over the optimal way to search a dataset. For % instance, Datasets with binary features may require fewer age layers and % fewer generations between novel generations; while datasets with % continuous or ordinal features may require more age layers and more % generations between novel generations. Param.NumNewPopNumFeat; % The # of new offspring created every Param.GENn Param.TotGens30; % Total # generations to run the CCEA % Param.FeatLabels[]; % The feature labels (not needed for CCEA but % necessary for understanding the features) Param.BestFitfalse(); % Will record the best hypergeometric fitness for % each CC order each generation Param.ALna5; % The # of layers that are not archived % (helps maintain diversity) Param.GENn3; % The # of generations until a new population of offspring % are created. Param.NonArchLMaxParam.NumNewPop*1;% Max population per non-archive layer Param.ArchOffParam.NonArchLMax*Param.ALna; %The max # of Archive offspring %created each generation Param.Px0.5; % Probability of crossover Param.Pwc0.75; % probability that feature selected for mutation will be % removed from the conjunctive clause Param.Pm1/NumFeat; % probability that a feature will be selected for % mutation. Only if the parent is selected for mutation % instead of crossover. Param.TournSize3; % # of parents with replacement that are in the % tournament to mate with the parent. Only most fit will % mate. 3 参考文献 文章中一些内容引自网络会注明出处或引用为参考文献难免有未尽之处如有不妥请随时联系删除。 [1]古华茂,石锦芹,高济.基于子句的ALCN语言tableau算法增强方式[J].东南大学学报(英文版), 2008.DOI:JournalArticle/5af28551c095d718d8f5e7c5.
[2]姚明臣.机器学习和神经网络学习中的若干问题研究[D].大连理工大学,2016.
4 Matlab代码实现
