Today, big data processing has become a challenging task due to the amount of data collected using various sensors increasingly significantly. To build knowledge and predict the data, traditional data mining methods calculate all numerical attributes into the memory simultaneously. The data stream method is a solution for processing and calculating data. The method streams incrementally in batch form; therefore, infrastructure memory is sufficient to develop knowledge. The existing method for data stream prediction is FIMT-DD (Fast Incremental Model Tree-Drift Detection). Using this method, knowledge is developed in tree form for every instance. In this paper, enhanced FIMT-DD is proposed using ARDEV (Average Restrain Divider of Evaluation Value). ARDEV utilizes the Chernoff bound approach with error evaluation, improvement in learning rate, modification of perceptron rule calculation, and utilization of activation function. Standard FIMT-DD separates the tree formation process and perceptron prediction. The proposed method evaluates and connects the development of the tree for knowledge formation and the perceptron rule for prediction. The prediction accuracy of the proposed method is measured using MAE, RMSE and MAPE. From the experiment performed, the utilization of ARDEV enhancement shows significant improvement in terms of accuracy prediction. Statistically, the overall accuracy prediction improvement is approximately 6.99 % compared to standard FIMT-DD with a traffic dataset.
- Big data prediction
- Tree regression