Weka中的堆叠算法是什么?它实际上是如何工作的? [英] What is the Stacking Algorithm in Weka? How it actually is working?

问题描述

基础分类器的结果是由投票系统选择的吗?那么元分类器在输入时实际上得到了什么，整个分类器还是只是未分类的分类器?如果整个机制可以用一个像这个链接这样的简单例子来解释，那将会很有帮助Weka.classifiers.meta.vote 中的多数投票算法

Is the result of Base classifiers are being selected by voting system & then what actually the Meta classifier is getting as it's input,whole classifier or just the miss-classified ones ? It would be helpful if the whole mechanism can be explained with a simple example like this link Majority vote algorithm in Weka.classifiers.meta.vote

提前致谢.

推荐答案

考虑一个由 n 个成员组成的集合.这些成员中的每一个都接受了一组给定的训练数据的训练.集成成员可以共享相同的分类器类型(同质)或使用不同的分类器(异质).鼓励成员之间的多样性，以便每个成员产生不同的估计.

Consider an ensemble of n members. Each of these members are trained on a given set of training data. The ensemble members may share the same classifier type (homogeneous) or use different classifiers (heterogeneous). Diversity is encouraged between the members so that each member generates different estimates.

堆叠不是对每个集成成员的估计进行投票或平均以生成集成估计，而是在集成成员和集成估计之间训练元学习器，该集成估计接受每个集成成员的估计作为输入并生成集成输出.

Instead of voting or averaging the estimates of each ensemble member to generate the ensemble estimate, stacking trains a meta-learner between the ensemble members and the ensemble estimate that accepts each ensemble member's estimate as inputs and generates the ensemble output.

下面概述了一个示例:

Ensemble Member 1 Estimate --->
Ensemble Member 2 Estimate --->  Meta-Learner -> Ensemble Estimate
Ensemble Member 3 Estimate --->

在 Weka 的情况下，classifiers"参数是指最初训练以估计输出的集成成员，而metaClassifier"是采用分类器输入并估计集成输出的模型.

In the case of Weka, the 'classifiers' parameter refers to the ensemble members that are initially trained to estimate the output, and the 'metaClassifier' is the model that takes the classifier inputs and estimates the ensemble output.

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