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!
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 $[1,1,2,0,0,0,0,0,0,0]$
...