Polish (Poland)English (United Kingdom)

Rough k nearest neighbours for classification in the case of missing input data

Type of Publication: In Book
Year: 2014
Pages: 196–207
Algorithm k-nn is often used for classification, but the majority of used distance meters are not designed to work with missing values. In the most appliances, this issue is solved using marginalization. Unfortunately, this approach can cause that even a large part of possessed data may be wasted. Also, after filling some of the missing values with valid data, the system may give completely different answer. Therefore in the paper a new algorithm is proposed, which in case of lacks in the sample analyses whole domain of possible values for corresponding attributes. Proposed system using k-nn algorithm gives roughspecific answer, which states if the test sample may or must belong to the certain set of classes. The system has important feature, that after filling some part of missing values it indicates the same set of possible classes or its subsets.

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