Data Mining of Perishable Food Safety Sampling based on Voting

Abstract

Different models had been made, which by selecting the neural network algorithm, classification and regression tree algorithm and Bayesian network algorithm in the data mining software. Then the three concrete models were combined and the conditional statements were derived from the derived nodes. According to the principle that the minority is subordinate to the majority, an accurate and credible forecasting model had been built by the way of "vote for". The prediction model can predict the condition of perishable food, which innovatively guiding the safety inspection work; by choosing the safety rapid detection for typical effective sample of perishable food, effectively improve the efficiency and effectiveness of the inspection work. The prediction model avoids the deterioration of perishable food flowing into the market, and ensures the safe transportation of perishable food.