List of numbers as classifier input
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 $[1,1,2,0,0,0,0,0,0,0]$
...