Name
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. |
Year | Venue | Keywords |
|---|---|---|
2008 | ESANN | mutual information,machine learning |
Field | DocType | Citations |
Drawback,Data mining,Pattern recognition,Computer science,Artificial intelligence,Mutual information,Data classification,Machine learning | Conference | 3 |
PageRank | References | Authors |
0.39 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro J. García-Laencina | 1 | 275 | 14.14 |
| José-Luis Sancho-Gómez | 2 | 182 | 17.26 |
| Aníbal R. Figueiras-Vidal | 3 | 467 | 38.03 |
| Michel Verleysen | 4 | 2291 | 221.75 |