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Cancer molecular subtype classification from hypervolume-based discrete evolutionary optimization

  • S.I.: 2018 India Intl. Congress on Computational Intelligence
  • Published:
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Abstract

High dimensionality and sample imbalance of gene expression data promote the development of effective algorithms for classifying gene expression data. To improve the ability to distinguish different subtypes of gene expression data, we devise a hypervolume-based discrete evolutionary optimization algorithm (HYBDEOA) in this paper. Four objectives, namely the number of genes, the accuracy, the relevance, and the redundancy, are optimized simultaneously to guide the evolution. Firstly, binary encoding is used to choose some features, projecting data onto different subspaces. After that, a discrete neighborhood operation is conducted to generate a new binary-mapped population. Combining the new population with the current population, we employ the hypervolume-based mechanism to select the Pareto solutions. Finally, a discrete mutation method is proposed to find promising solutions in the binary search space. To demonstrate the performance of HYBDEOA, we apply HYBDEOA to 55 synthetic datasets and 35 cancer gene expression datasets. Extensive experiments are also conducted to reveal the effectiveness and efficiency of HYBDEOA. The experimental results demonstrate that our proposed method is a parameter-less and robust algorithm, which can group gene expression data with a finer and more informative classification.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China under Grant No. 61603087, funded by the Natural Science Foundation of Jilin Province under Grant No. 20190103006JH, and the Science and Technology Development Planning of Jilin Province No. 20160204043GX. The work described in this paper was substantially supported by two grants from the Research Grants Council of the Hong Kong Special Administrative Region [CityU 11203217] and [CityU 11200218] and the funding from Hong Kong Institute for Data Science (HKIDS) at City University of Hong Kong. The work described in this paper was partially supported by a grant from City University of Hong Kong (CityU 11202219).

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Authors and Affiliations

  1. School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China

    Yunhe Wang, Shaochuan Li, Lei Wang & Zhiqiang Ma

  2. School of Artificial Intelligence, Jilin University, Changchun, 130012, China

    Xiangtao Li

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  1. Yunhe Wang
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Wang, Y., Li, S., Wang, L. et al. Cancer molecular subtype classification from hypervolume-based discrete evolutionary optimization. Neural Comput & Applic 32, 15489–15502 (2020). https://doi.org/10.1007/s00521-020-04846-2

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