Abstract
A new concept of a quantum-like mixture model is introduced. It describes the mixture distribution with the assumption that a point is generated by each Gaussian at the same time. The quantum-like mixture Gaussian improves the classification accuracy in machine learning by indicating that the uncertain points should not be assigned to any class. It increases the accuracy of the mixture Gaussian model on the iris data set from 96.67 to 99.24%.
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Acknowledgements
We would also like to thank the anonymous reviewer for their valuable feedback. This work was supported by national funds through FCT, Fundação para a Ciência e a Tecnologia, Under Project UIDB/50021/2020.
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The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors declare no conflicts of interest. This article does not contain any studies with human participants or animals performed by any of the authors.
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Wichert, A. Quantum-like Gaussian mixture model. Soft Comput 25, 10067–10081 (2021). https://doi.org/10.1007/s00500-021-05941-9
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DOI: https://doi.org/10.1007/s00500-021-05941-9