Novel Rough Neural Network for Classification with Missing Data
Rodzaj publikacji: | Konferencja | ||
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Rok: | 2016 | ||
Autorzy: |
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Tytuł książki: | Methods and Models in Automation and Robotics (MMAR), 2016 21th International Conference on | ||
Strony: | 820-825 | ||
Miesiąc: | August | ||
BibTex: |
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Abstrakt: | 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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