On accuracy of PDF divergence estimators and their applicability to representative data sampling.

Budka, M., Gabrys, B. and Musial, K., 2011. On accuracy of PDF divergence estimators and their applicability to representative data sampling. Entropy, 13 (6), pp. 1229-1266.

Full text available as:

entropy-13-01229.pdf - Published Version


Official URL: http://www.mdpi.com/1099-4300/13/7/1229/

DOI: 10.3390/e13071229


Generalisation error estimation is an important issue in machine learning. Cross-validation traditionally used for this purpose requires building multiple models and repeating the whole procedure many times in order to produce reliable error estimates. It is however possible to accurately estimate the error using only a single model, if the training and test data are chosen appropriately. This paper investigates the possibility of using various probability density function divergence measures for the purpose of representative data sampling. As it turned out, the first difficulty one needs to deal with is estimation of the divergence itself. In contrast to other publications on this subject, the experimental results provided in this study show that in many cases it is not possible unless samples consisting of thousands of instances are used. Exhaustive experiments on the divergence guided representative data sampling have been performed using 26 publicly available benchmark datasets and 70 PDF divergence estimators, and their results have been analysed and discussed.

Item Type:Article
Uncontrolled Keywords:cross-validation; divergence estimation; generalisation error estimation; Kullback-Leibler divergence; sampling
Subjects:Generalities > Computer Science and Informatics > Artificial Intelligence
Generalities > Computer Science and Informatics
Group:Faculty of Science & Technology
ID Code:18405
Deposited By: Dr Marcin Budka
Deposited On:11 Aug 2011 11:28
Last Modified:10 Sep 2014 14:52


Downloads per month over past year

More statistics for this item...
Repository Staff Only -