Kernlab中的类权重语法? [英] Class Weight Syntax in Kernlab?

问题描述

我正在尝试使用kernlab包对R中的不平衡数据集进行分类，因为类分布不是1:1，所以我在ksvm()函数调用中使用了class.weights选项，但是我没有得到添加权重或删除权重时，分类方案有何不同?因此，问题是声明类权重的正确语法是什么?

Hi I am trying out classification for imbalanced dataset in R using kernlab package, as the class distribution is not 1:1 I am using the option of class.weights in the ksvm() function call however I do not get any difference in the classification scenario when I add weights or remove weights? So the question is what is the correct syntax for declaring the class weights?

我正在使用以下函数调用:

I am using the following function calls:

model = ksvm(dummy[1:466], lab_tr,type='C-svc',kernel=pre,cross=10,C=10,prob.model=F,class.weights=c("Negative"=0.7,"Positive"=0.3)) 
#this is the function call with class weights 
model = ksvm(dummy[1:466], lab_tr,type='C-svc',kernel=pre,cross=10,C=10,prob.model=F) 

任何人都可以对此发表评论，我是否遵循增加权重的正确语法?我还发现，如果我们将权重与prob.model=T一起使用，则ksvm函数将返回错误！

Can anyone please comment on this, am I following the right syntax of adding weights? Also I discovered that if we use the weights with prob.model=T the ksvm function returns a error!

推荐答案

您的语法还可以，但是在机器学习中，不平衡类的问题相当普遍.从某种意义上说，从较大的类中删除某些对象是保证工作的唯一方法，但它仍然可能是错误增加的根源，并且必须谨慎地以智能方式进行操作(在SVM中，潜在的支持向量应该具有优先权-当然，现在有一个问题如何定位它们.)
您还可以尝试通过简单的长度比率来增加权重，比如说十倍，然后检查它是否甚至稍微或幸运地帮助了另一侧的不平衡.

Your syntax is ok, but the problem of not-working-class-balance is fairly common in machine learning; in a way, the removal of some objects from the bigger class is an only method guaranteed to work, still it may be a source of error increase, and one must be careful to do it in an intelligent way (in SVM the potential support vectors should have the priority - of course now there is a question how to locate them).
You may also try to boost the weights over simple length ratio, lets say ten-fold, and check if it helped even a little or luckily rather overshoot the imbalance to the other side.

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