[D] Is there a way for a Neural Net (or other approach) to handle interchangeable features explicitly?
Let’s say I have 3 features describing some value of 3 objects of importance. The 3 objects are identical.
In my current model, the three objects are basically randomly assigned to be the features object_0, object_1, object_2.
It doesn’t matter for the real life outcome which object is 0, 1 , or 2. Of course, the neural net will learn that these are interchangeable in theory, but is there a way people have been doing this in a more explicit way?
Would it improve training to, for example, always order the values from largest to smallest, 0-2? Or some manipulation like that?
If this is a terrible question or you have corrections about the way I asked it, feel free to let me know. I am still learning.