使用熵的加权决策树 [英] Weighted Decision Trees using Entropy

问题描述

我正在使用互信息增益作为拆分功能来构建二进制分类树.但是，由于训练数据偏向几个班级，因此建议按每个班级的倒数对每个训练示例加权.

I'm building a binary classification tree using mutual information gain as the splitting function. But since the training data is skewed toward a few classes, it is advisable to weight each training example by the inverse class frequency.

我如何加权训练数据?在计算估计熵的概率时，我是否采用加权平均值?

How do I weight the training data? When calculating the probabilities to estimate the entropy, do I take weighted averages?

我想要一个带有权重的熵表达式.

I'd like an expression for entropy with the weights.

推荐答案

状态值加权熵作为投资风险的度量.
http://www56.homepage.villanova. edu/david.nawrocki/State％20Weighted％20Entropy％20Nawrocki％20Harding.pdf

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