如何选择合适的机器学习分类器
我在为数据挖掘任务选择正确的分类器时遇到问题。
我使用统计方法来标记网页，并使用 1-4 等级来标记它们，1 是最差的，4 是最好的。
之前，我使用 SVM 来训练系统，因为当时我使用的是二进制 (1,0) 标签。但现在自从我切换到这个 4 类标签后，我需要更改分类器，因为我认为 SVM 分类器仅适用于二类分类（如果我错了，请纠正我）。
哪种分类器最适合我的分类目的？
I am facing a problem on selecting a correct classifier for my data-mining task.
I am labeling webpages using statistical method and label them using a 1-4 scale, 1 being the poorest while 4 being the best.
Previously, I used SVM to train the system since I was using a binary (1,0) label then. But now since I switch to this 4-class label, I need to change classifier, because I think the SVM classifier will only work for two-class classification (correct me if I am wrong).
What kind of classifier is most appropriate here for my classification purpose?
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存在多类SVM。 LibSVM 有一个实现，Weka。
通常最好尝试多个分类器，以找出最适合您的数据的分类器。分类器类型和训练算法的选择远没有特征集的选择那么重要。您可以尝试朴素贝叶斯、多类 SVM、MaxEnt、投票感知器或您的库提供的任何工具。
There exist multi-class SVMs. LibSVM has an implementation, as does Weka.
Usually it's better to experiment with several classifiers to find out which one works best on your data. The choice of classifier type and training algorithm is far less important than your choice of feature set. You could try naïve Bayes, multi-class SVM, MaxEnt, voted perceptrons, or whatever your library offers.
你说的是“序数分类”。可以使用 SVM 进行修改（如已经提到过，它也在 libSVM 中实现），使用逻辑回归，甚至使用决策树，或人工神经网络。
您甚至可以继续标签，执行您选择的回归分析，然后对输出进行离散化。我上面提到的大多数方法都是在幕后进行的。
祝你好运
You are talking about "ordinal classification". It can be done modified using SVM (as already mentioned, it is also implemented in libSVM), using logistic regression, and even using decision trees, or artificial neural networks.
You can even continuize your labels, perform regression analysis of your choice, and then descretize the output. Most of the methods I have mentioned above do that behind the scenes.
Good luck
您可以尝试查看 Andrew NG 讲座，了解如何选择最适合您的 ML 算法，我认为很有启发性，它可能会给您一些关于如何管理数据的见解
You might try to check Andrew NG Lecture on how to choose the ML algorithm that bests suits you, I think is quite enlightening, and it might give you some insight on how to manage your data