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.
CITATION STYLE
Tran, C. T., Zhang, M., Xue, B., & Andreae, P. (2018). Genetic programming with interval functions and ensemble learning for classification with incomplete data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11320 LNAI, pp. 577–589). Springer Verlag. https://doi.org/10.1007/978-3-030-03991-2_53
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