# List of numbers as classifier input

I am trying to use my data to predict the classes of the input. My data are as the following:

x1 = [0.2, 0.25, 0.15, 0.22] y = 1
x2 = [0.124, 0.224, 0.215, 0.095] y = 3
...
xn = [...] y = 2


The problem is that my data are just lists of numbers that do not have an order. I mean that x1 can be x1 = [0.2, 0.25, 0.15, 0.22] y = 1 or x1 = [0.25, 0.22, 0.2, 0.15] y = 1 or the numbers in the list to be in any other order.

Is there anything that I can do, so I will be able to build a classifier? Thank you!

• If the order doesn't matter, it seems like you have repeated measurements of a single feature for each sample. Can you build a classifier to classify each measurement individually, and then take a consensus of the predictions? – Nuclear Hoagie Jul 7 '20 at 16:18

The simple option is to design your features so that they represent the distribution of the values: every feature $$f_i$$ represents a bin and its value for a particular instance is the frequency of the corresponding range for this instance.
Example: let's consider 10 bins between 0 and 1, i.e. $$f_1=[0,0.1), f_2=[0.1,0.2),..., f_{10}=[0.9,1]$$:
• $$x_1=[0.2, 0.25, 0.15, 0.22]$$ is represented as $$[0,1,3,0,0,0,0,0,0,0]$$
• $$x_2 = [0.124, 0.224, 0.215, 0.095]$$ is represented as $$[1,1,2,0,0,0,0,0,0,0]$$