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I have constructed SVMs to do a one-vs-many approach to classification. Let's say I have 3 classes and I train 3 SVMs in a one-vs-many format. This gives me 3 SVMs each trained positively on one of a class {a,b,c} and trained negatively on the remaining data.

When testing a test sample of class a, I may get results looking like:

class a as positive SVM:
a: 0.6 neg: 0.4
class b as positive SVM:
b: 0.1 neg: 0.9
class c as positive SVM:
c: 0.2 neg: 0.8

Clearly the sample does belong to class a. I wish to use a probability however. I'm wondering what to use for this? If I use the highest probability, then it is not necessarily very high. We could has class a SVM giving probability of 0.2 and the two others as 0.0001, using 0.2 as the probability doesn't seem relative. Is there a way to get a probability using the one-vs-all technique that factors this information in? If not, then thresholds of, e.g.: 0.6, on acceptance of a class many not be met by any score and nothing may be over the threshold enough to say this sample belongs with this class.

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Here's what I would recommend: Use probability rankings and class proportions in the training sample to determine the class assignments.

You have three (estimated) probabilities: $p_a, p_b,$ and $p_c$. And you have the original class proportions from the training sample: $m_a, m_b,$ and $m_c$, where $m_a$ is the percentage of classes that belong to class $a$ (e.g., 0.6), and so on.

You can start with the smallest class, say $b$, and use $p_b$ to rank order all records from the highest to lowest values. From this rank-ordered list, start assigning each record to class $b$ until you have $m_b$ percent records assigned to this class. Record the value for $p_b$ at this stage; this value will become the cut-off point for class $b$.

Now take the next smallest class, say $c$, and use $p_c$ to rank order all records and follow the same steps described in the paragraph above. At the end of this step, you will get a cut-off value for $p_c$, and $m_c$ percent of all records would be assigned to class $c$.

Finally, assign all remaining records to (the largest) class $a$.

For future scoring purposes, you can follow these steps but discard the class proportions. You can let the probability cut-off values for class $b$ and $c$ to drive class assignments.

In order to make sure that this approach yields a reasonable level of accuracy, you can review the classification matrix (and any other measures you are using) on the validation set.

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  • $\begingroup$ Thank you, very informative. I will look into this! It gives a good starting point for me to look into. $\endgroup$ – mino Apr 24 '16 at 12:03
  • $\begingroup$ Please do share your findings (accuracy etc.) if and when you can. $\endgroup$ – Vishal Apr 25 '16 at 18:01
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What you want to do is probability estimation through pairwise coupling. Check the paper by Zadrozny B. (2001). Here's the Abstract:

This paper presents a method for obtaining class membership probability estimates for multiclass classification problems by coupling the probability estimates produced by binary classifiers. This is an extension for arbitrary code matrices of a method due to Hastie and Tibshirani for pairwise coupling of probability estimates. Experimental results with Boosted Naive Bayes show that our method produces calibrated class membership probability estimates, while having similar classification accuracy as loss-based decoding, a method for obtaining the most likely class that does not generate probability estimates.

Zadrozny, B. (2001). Reducing multiclass to binary by coupling probability estimates. In Advances in neural information processing systems (pp. 1041-1048).

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