Perceptron rule improvement on FIMT-DD for large traffic data stream

This paper proposed a method to build knowledge from one and a half years of UK traffic data sets. The method used is the Fast Incremental Model Trees - Drift Detection (FIMT-DD) with an improvement on the perceptron rule. In order to predict a traditional data set, we first analyze the model. After we have analyzed the model, we then average it from different arrangements of the datasets. In a stream data set, the approach is different from the traditional data sets. The approach of a stream data set is to take several snapshots during the induction to analyze the accuracy progress of the predicted model. We used the tanh activation function to optimize the perceptron rule. Therefore, we are able to reduce the error of the result. The error measurements that we analyzed in this paper are MAE, RMSE, and SMAPE. From 100,000,000 instances of traffic data, the optimized FIMT-DD algorithm method proves to be successful with smaller error value than standard FIMT-DD algorithm. To be able to measure the performance of the error of our optimized FIMT-DD algorithm, we used evaluate prediction sequential to analyze the data. According to the error measurement results of the MAE, RMSE, and SMAPE, tanh(x) activation function has a good influence to decrease the error value and improve the accuracy. The smaller error values did not only happen in a few experiments instances, but it occurs in the majority of the instances evaluation.