K-nearest neighbours based on mutual information for incomplete data classification
(2008) 16th European Symposium on Artificial Neural Networks (ESANN 2008) — Location: Bruges (Belgium) (23.May.2008)
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- Authors
- Abstract
- Incomplete data is a common drawback that machine learning techniques need to deal with when solving real-life classification tasks. One of the most popular procedures for solving this kind of problems is the K-nearest neighbours (KNN) algorithm. In this paper, we present a weighted KNN approach using mutual information to impute and classify incomplete input data. Numerical results on both artificial and real data are given to demonstrate the effectiveness of the proposed method.
- Affiliations
Universidad Politecnica de Cartagena Dpto TIC Universidad Politecnica de Cartagena Dpto TIC Universidad Carlos III de Madrid Dpto TSC
Citations
Garcia-Laencina, P., Sancho-Gomez, J.-L., Figueiras-Vidal, A. R., & Verleysen, M. (2008). K-nearest neighbours based on mutual information for incomplete data classification. Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN 2008), p. 25-30. https://hdl.handle.net/2078.5/254140