K-nearest neighbours based on mutual information for incomplete data classification

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.

• Type: Conference
• Year: 2008
• Where: ESANN
• Authors: Pedro J. García-Laencina, José-Luis Sancho-Gómez, Aníbal R. Figueiras-Vidal, Michel Verleysen
• Keywords: ESANN 2008, Incomplete Input Data, Neural Networks, Real-life Classification Tasks, Weighted Knn Approach