问题描述
我已经训练了MultiClassClassifier(经过测试，可以正常工作)并将其保存在硬盘上的某个位置.现在，我要对我得到的新样品进行预测.我加载了应用程序，分类器自动加载了该应用程序.在分类过程之外，我已经将样本的搜索范围缩小到五个5个可能的类.我知道k类，这很容易在分类中避免.
I have trained a MultiClassClassifier (tested, working) and saved it somewhere on my hard drive. Now I want to make predictions for a new sample I got. I load my application and my classifier auto loads with it. I have narrowed down the search to five 5 possible classes already for the sample, outside the classification process. This means, I know k classes, that can easily be avoided in the classification.
是否可以在使用MultiClassClassifier之前对其进行过滤(过滤掉所有不需要的类)?
如果是这样?为此目的，可以使用什么Weka方法? 如果不是，是否有其他更改.解决方案?
If it is? What would be the Weka method to work with for this purpose? If not, is there an alter. solution?
我想通过将重点放在n类中的5个类上来提高分类器的准确性.
I want to increase the accuracy of the classifier by narrowing down the focus on 5 classes out of n classes.
我已经找到了如何过滤Instances对象的方法，但似乎找不到适合MultiClassClassifer的方法.
I've found how to filter Instances objects but can't seem to find a proper method for the MultiClassClassifer.
我要处理的数据是我的测试Instances和我的MultiClassClassifier.
My data to manipulate with is/are my testing Instances and my MultiClassClassifier.
先谢谢您.
推荐答案
实际上并没有一种方法可以修改现有的MultiClassClassifier来排除类.但是，根据您使用的基础分类器，您可以尝试使用.distributionForInstance，它输出置信度得分的向量，每类一个.然后，您可以选择得分最高的班级，而忽略目标集中未包含的班级的得分.
There isn't really a way to modify an existing MultiClassClassifier to exclude classes. However, depending on the underlying classifier you're using, you could try using .distributionForInstance which outputs a vector of confidence scores, one per class. You could then take the class with the highest score, ignoring the scores for the classes not in your target set.
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