Which method should be used to determine the class ID of multiple SVM models?

Question

I'm using Support Vector Machine(SVM) with image classification. Each SVM model results a linear model

$$y = wx + b$$

Where $w$ and $b$ is the SVM parameters.

If I have multiple SVM models, I will get a vector $Y$ and matrix $W$ and vector $B$

$$Y = Wx + B$$

In my current case, I have a activation function called sign

$$Y = sign(Wx + B)$$

Now, $Y$ can holds values such as 1 or -1 only. In my case, $Y$ has $n$ dimensions (rows) and therefore $W$ and $B$ are a $n$-class SVM model. The index of $Y$ that holds the number 1, is the class ID of vector $x$.

The problem:

The problem is that what if I get two number 1 inside vector $Y$ like $Y = [-1, -1, 1, -1, -1, -1, 1, -1, -1, -1, -1]$ ?

I can solve this by using propability. But my data is small, about 150-200 $Y$ vectors.

Assume that we are removing the activation function sign and using the pure data of $Y$ instead. Which method sould I use here to determine which $Y_i$ for a given $x_i$, is giving the user the class ID of vector $x_i$?

1. Decision trees (multiple if-statements)
2. Naive Bayes Classifier
3. Markov chains
4. Linear regression
5. Other?

The goal here is to find an integer number as class ID for vector $x$.