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I am learning the SVM classification and especially interested in applying to medical data. Now, I encounter a problem and do not know if this dilemma has a terminology for it.

Assume that there are samples of healthy people ( of both gender) and people with liver cancer ( of both gender). If we label healthy people sample as class 1 and the people with cancer as class 2, we can train a binary SVM and obtain a classifier 1 to predict any new patient. Now, image another scenario. Assume that we first divide all samples by gender into two groups, one for female and the other for male. For each gender, we still label healthy patients vs cancerous patients into 2 classes and train a binary SVM to obtain classifier 2 and classifier 3 for female and male samples respectively. The question is if there is a new female patient, which classifier, 1 or 2, should be used to obtain more accurate prediction ? Here is the dilemma for the arguments I have

(1) When the number of samples is large, the prediction should be more accurate. Based on this argument, the classifier 1 seems a good choice.

(2) However, if we divide samples into female and male groups first, the classifier 2 seems a better choice since the new patient (unknown test sample) is female.

Does this kind of dilemma have a terminology or does anyone know any further information or how to solve problem like this ? I am not even sure if this is a legit question and sorry for the naive question in advance. Thanks

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migrated from Mar 6 '13 at 3:44

This question came from our site for theoretical computer scientists and researchers in related fields.

Sure~ Thanks you very much. – Cassie Mar 6 '13 at 2:36
If gender is treated as a feature in classifier 1, then at some level the SVM algorithm itself solves this issue for you. i.e. gender would be used where and when needed. I know this is not exactly what you're asking, but my 2 cents. – etov Mar 6 '13 at 11:53

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