Improving Knowledge Acquisition Capability of M5´ Model Tree on Small Datasets

In many small dataset learning tasks, however, owing to the incomplete data structure, the explicit information for decision making is limited. This research aims to learn more information hidden inside the incomplete data by adding more samples to strengthen data structures. Based on the prior knowledge provided by the M5´ model tree, the proposed research mechanism generates artificial samples to enhance the crisp data structures. Besides, the ability to handle nominal attributes is also provided in this research, while it is usually lacking in most sample generation approaches. In the experiments, the results show that the knowledge acquisition capability and the predictive accuracies of M5´ are improved.