Abstract
Classification tasks become more challenging when having the curse of dimensionality issue. Recently, there has been an increasing number of datasets with thousands of features. Some classification algorithms often need feature selection to avoid the curse of dimensionality. Genetic programming (GP) has shown success in classification tasks. GP does not require to do feature selection because of its built-in capability to automatically select informative features. However, GP-based methods are often computationally intensive to achieve a good classification accuracy. Based on perspectives from granular computing (GrC), this paper proposes a new approach to linking features hierarchically for GP-based classification. Experiments on seven high-dimensional datasets show the effectiveness of the proposed algorithm in terms of saving training time and enhancing the classification accuracy, compared to baseline methods.
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Acknowledgement
This work was supported in part by the Marsden Fund of New Zealand Government under Contracts VUW1209, VUW1509 and VUW1615, Huawei Industry Fund E2880/3663, Natural Science Foundation of Jiangsu, China BK20161406, and the University Research Fund at Victoria University of Wellington 209862/3580, and 213150/3662.
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Pei, W., Xue, B., Shang, L., Zhang, M. (2018). Genetic Programming Based on Granular Computing for Classification with High-Dimensional Data. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_58
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