同质与异质合奏 [英] Homogeneous vs heterogeneous ensembles

问题描述

我想与您确认我对整体学习(同质还是异质)的理解是否正确.

I would like to check with you if my understanding about ensemble learning (homogeneous vs heterogeneous) is correct.

以下陈述正确吗?

同质系综是一组基于不同数据(如随机森林)的相同类型的分类器，而异质系综是基于此分类的不同类型的分类器相同的数据.

An homogeneous ensemble is a set of classifiers of the same type built upon different data as random forest and an heterogeneous ensemble is a set of classifiers of different types built upon same data.

如果不正确，请您说明一下这一点吗?

If it's not correct, could you please clarify this point?

推荐答案

同质集成由具有单一类型基本学习算法的成员组成.打包和增强等流行方法会生成 通过从训练中采样或分配权重来实现多样性 示例，但通常使用单一类型的基本分类器 建立合奏.

Homogeneous ensemble consists of members having a single-type base learning algorithm. Popular methods like bagging and boosting generate diversity by sampling from or assigning weights to training examples but generally utilize a single type of base classifier to build the ensemble.

另一方面，异构集成由具有不同基础学习算法(例如SVM，ANN和决策树)的成员组成.流行的异类集成方法是堆叠，这类似于增强.

On the other hand, Heterogeneous ensemble consists of members having different base learning algorithms such as SVM, ANN and Decision Trees. A popular heterogeneous ensemble method is stacking, which is similar to boosting.

此表包含同质和异质集成模型的示例.

This table contains examples for both homogeneous and heterogeneous ensemble models.

同类集成方法，使用具有不同训练数据的相同特征选择方法，并将数据集分布在多个节点上，而 异构集成方法使用具有相同训练数据的不同特征选择方法.

Homogeneous ensemble methods, use the same feature selection method with different training data and distributing the dataset over several nodes while Heterogeneous ensemble methods use different feature selection methods with the same training data.

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