Which classifier is more accurate for a SVM classification?

I am learning the SVM classification and encounter a problem. I am not sure if this dilemma has a terminology for it.

Assume we would like to classify patient by SVM given the 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 before SVM classification. 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

• This can't be answered in general. Perhaps if we knew how much gender influences cancer and how many samples you have, which loss function you use etc. It's probably much easier to experiment using cross validation. Commented Mar 6, 2013 at 9:51
• Thanks. It makes sense. I guess there should not be a general rule. Commented Mar 8, 2013 at 22:15
• this sounds like a general ML question about "how should I go about using ML to solve this problem". there is no standard answer. its important/accepted/standard to try different approaches and see which strategies lead to the most accurate prediction results. the general heading is something like "representation of the real world problem in the abstract ML framework" or roughly "modelling" & is covered in good std refs.... see also stats.se
– vzn
Commented Sep 23, 2013 at 17:59