Method to combine multiple svm classifiers (or "any ML classifier" by using scikit-learn. "decision-feature classifiers"
First of all - the idea of training separate models is rather bad. Unless you have very good reasons to do so (some external limitations you cannot ignore) you should not do so. Why? Because you are efficiently loosing information, you are unable to model complex dependencies between signals from two classifiers. Training everything jointly gives statistical method ability to choose which data use when, this way it can model for example - that for some particular types of data it will use one part of the input and for another - the rest. When you build independent classifiers - you bias the whole process, as these classifiers have "no idea" that remaining ones exist.
Having said that, here is the solution assuming that somehow you cannot learn joint model. In such scenario (where your models are kind of black-boxes which convert your input representation to decision functions) the basic idea is to simply treat them as preprocessors and fit new model on top, thats all. In other words you have your data point x split into feature-vectors x1,x2,...,xk and k different models mi you built previously, thus you use them to construct a preprocessing method f(x) = [m1(x1), m2(x2), ..., mk(xk)] and this is just a single point in R^k space, which can be now fitted into new classifier to learn how to combine these information. The problematic part is that due to your very specific process, you now need new training set to learn combination rule, as using the same data that was used to construct mi can easily lead to overfitting. To fight that people sometimes use heuristic approaches instead - a priori assuming that these models are already good enough and construct ensemble of these, which either vote for classes (for example weighted by their certeinty) or build whole system around it. I would still argue that you should not go this way in the first place, if you have to - go with learning combination rule with the use of new data, and finally if you cannot do any of the above - settle with some heuristic ensemble technique.
I extract multiple Feature vectors from different sensors, and I trained these features by using SVM individually. My question is there any method to combine these classifiers in a way to obtain a better result. 
thanks in advance