Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Kernel Principal Component Analysis
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
An Evaluation of k-Nearest Neighbour Imputation Using Likert Data
METRICS '04 Proceedings of the Software Metrics, 10th International Symposium
Nearest neighbour approach in the least-squares data imputation algorithms
Information Sciences: an International Journal
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Impact of imputation of missing values on classification error for discrete data
Pattern Recognition
| Hi-index | 0.07 |
The possibility of missing or incomplete data is often ignored when describing statistical or machine learning methods, but as it is a common problem in practice, it is relevant to consider. A popular strategy is to fill in the missing values by imputation as a pre-processing step, but for many methods this is not necessary, and can yield sub-optimal results. Instead, appropriately estimating pairwise distances in a data set directly enables the use of any machine learning methods using nearest neighbours or otherwise based on distances between samples. In this paper, it is shown how directly estimating distances tends to result in more accurate results than calculating distances from an imputed data set, and an algorithm to calculate the estimated distances is presented. The theoretical framework operates under the assumption of a multivariate normal distribution, but the algorithm is shown to be robust to violations of this assumption. The focus is on numerical data with a considerable proportion of missing values, and simulated experiments are provided to show accurate performance on several data sets.