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Novel Rough Neural Network for Classification with Missing Data

Type of Publication: In Proceedings
Year: 2016
Book title: Methods and Models in Automation and Robotics (MMAR), 2016 21th International Conference on
Pages: 820-825
Month: August
The paper presents a new feedforward neural network architecture. Thanks to incorporating the rough set theory, the new network is able to process imperfect input data, i.e. in the form of intervals and with missing values. The paper focuses on the last case. In contrast to imputation, marginalisation and similar solutions, the proposed architecture is able to give an imprecise answer as the result of input data imperfection. In the extreme case, the answer can be indefinite contrary to a confabulation specific for the aforementioned methods. The results of experiments performed on three classification benchmark datasets for every possible combination of missing values, showed the proposed solution works well with missing data with accuracy dependent on the level of missing data.

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