Genetic Programming with Interval Functions and Ensemble Learning for Classification with Incomplete Data

Missing values are an unavoidable issue in many real-world datasets. Classification with incomplete data has to be addressed carefully because inadequate treatment often leads to a big classification error. Interval genetic programming (IGP) is an approach to directly use genetic programming to evolve an effective and efficient classifier for incomplete data. This paper proposes a method to improve IGP for classification with incomplete data by integrating IGP with ensemble learning to build a set of classifiers. Experimental results show that the integration of IGP and ensemble learning to evolve a set of classifiers for incomplete data can achieve better accuracy than IGP alone. The proposed method is also more accurate than other common methods for classification with incomplete data.