Support Vector classifier perform well with input features rather than transformed features in contrast to ANN-BP, random forest (other classifiers)
Thanks for the answer and it completely makes sense that results will be driven by data set in question!!

Is there any theory that says that SVM gives better results as compared to other ML algos like NN and RF when features are lower in number?
Another reason that I can think the reason for this behaviour is attributed to the information loss during transformations which were done.

Though SVMs results almost remained the same but for other algorithms improvement was significant(8-10%). Means transformed data showed patterns that RF and NN were able to recognize and do the classification while it hardly affected SVM outputs.