NaiveBayes tries to estimate the normal distribution of the data. If all attribute values are 0, which is the case within this sample process, the resulting normal distribution has mean 0 and variance 0, causing an infinite density at this value and all other values having density 0. Since NaiveBayes assumes independence between all attributes, the probabilities are multiplied. With one empty attribute, the product will become 0, causing the classification to only one class.
The svm seems to invert mappings.
That's for explantion. And now we will start working on it
Thanks for the hint.