Abstract
In this study, the K Nearest Neighbor's parameter k is predicted by system. Meta learning method is used for prediction. Getting training set with meta-features, 200 data sets were used. For each of them, 16 meta-features were extracted. The K Nearest Neighbour algorithm was applied each of them with most common 6 k values the best one is selected. With this training set it is possible to predict a new data set's best k value. In 200 data sets the most common k value which has best performance is 1. 4 methods are applied on the model. Generally all methods used same features and some meta-features are never used.
Keywords
Meta learning (computer science)
Computer science
k-nearest neighbors algorithm
Artificial intelligence
Set (abstract data type)
Training set
Data set
Value (mathematics)
Pattern recognition (psychology)
Selection (genetic algorithm)
Data mining
Machine learning
Engineering
Subject Areas
Bayesian Modeling and Causal Inference
·Artificial Intelligence ·Physical Sciences
Machine Learning and Data Classification
·Artificial Intelligence ·Physical Sciences
Data Mining Algorithms and Applications
·Information Systems ·Physical Sciences
Citations by Year
2015: 1
Loading…
Loading…