A Transaction Trade-Off Utility Function Approach for Predicting the End-Price of Online Auctions in IoT

Algorithm 1: The proposed TTUP algorithm

Inputs: auction training dataset , testing dataset , the total number of clusters

Outputs: classifying accuracy, KNeighborsRegressor model

Training Stage:

(1) For ; ++;

(2) {

(3)  transaction trade-off utility distance between any two auction items can be calculated by Equation (3)

(4)  classifying the training dataset into clusters

(5)  For ; ++;

(6)  {

(7)   get transaction trade-off utility of each cluster

(8)   get regression prediction price model for each cluster

(9)   }

(10) }

Test Stage:

(11) For ; ++;

(12) {

(13)   If (the transaction trade-off utility distance between test data and cluster )

(14)   test data belongs to cluster

(15)   Apply KNeighborsRegressor() to classify and forecast

(16)   Obtain the classification accuracy

(17)   Obtain RMSE

(18) }

Algorithm 1: The proposed TTUP algorithm