Handling incomplete quantitative data for supervised learning based on fuzzy entropy

In recent years, machine learning and knowledge discovery techniques have attracted a great deal of attention in the information area. Classification is one of the important research topics on these research areas. Most of the researches on classification concern that a complete data set is given as a training set and the test data know all values of attributes clearly. Unfortunately, incomplete data are commonly seen in real-world applications. In this paper, we propose a new strategy to deal with the incomplete quantitative data and introduce a supervised learning method based on genetic programming to handle the classification problem with incomplete data in the attributes. Two experiments are designed to evaluate the effectiveness of the proposed approaches.