Use the combination of the decision tree and the artificial neural networks to predict the outcome of table tennis matches

During several table tennis matches, the prediction of outcomes is of a major interest to coaches to arrange suitable and effective trainings. The purpose of this investigation is to propose a new approach of combination to predict the outcome of matches. The artificial neural network (ANN)is capable of efficient data fitting, as the decision tree is capable of data reduction and classification. We believe it´d a good thing to combine the two together. The article discussed the two algorithm´s characteristics and rose using a combo-prediction approach. Practices have shown that the algorithm is applicable. The new methods used data from 70 matches to develop predictive models of excellent ping-pong players and 32 matches for test. Combination prediction, which takes on average 3.1094s, takes only 18.61% of ANN would take. The accuracy is 0.9285; is close to ANN.