Ethereum Ponzi Scheme Detection Based on PD-SECR

Algorithm 1

Data preparation based on SMOTEENN.

	input: , δ is a random, k points nearest to s are denoted as Nk(s), epochs is the number of training wheels, y is the actual true value and is an estimate value
	output: The new dataset W after SMOTEENN processing
(1)	Generate new sample xnew and perform k_nearer neighbor calculation on xi;
(2)	Generate dataset W from a minority class samples of the input dataset Q;
(3)	for to range(m) do
(4)	Find the k minority class samples closer to xi;
(5)	W = [];
(6)	;
(7)	W.append (xnew);
(8)	return W;
(9)	Clean the newly generated dataset ;
(10)	Calculate the category t of s, according to the classification decision rules in Nk(s);
(11)	for to range(n) do
(12)
(13)	if or then delete it;
(14)	else do nothing;
(15)	return W processed by us;