CODEWITHSUNDEEP
artificial-intelligencemachine-learningdata-miningclassification
Kevin
Kevin

Reputation: 6831

How to choose the right machine-learning classifer

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?

Upvotes: 4

Views: 543

Answers (3)

Leon palafox
Leon palafox

Reputation: 2785

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

Upvotes: 1

Boris Gorelik
Boris Gorelik

Reputation: 31797

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

Upvotes: 2

Fred Foo
Fred Foo

Reputation: 363817

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.

Upvotes: 6

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