Research Article
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) } |
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Algorithm 1: The proposed TTUP algorithm |