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Using information on class interrelations to improve classification of multiclass imbalanced data: A new resampling algorithm

Janicka Małgorzata, Lango Mateusz, Stefanowski Jerzy

International Journal of Applied Mathematics and Computer Science

2019

Vol. 29, no. 4

769--781

The relations between multiple imbalanced classes can be handled with a specialized approach which evaluates types of examples’ difficulty based on an analysis of the class distribution in the examples’ neighborhood, additionally exploiting information about the similarity of neighboring classes. In this paper, we demonstrate that such an approach can be implemented as a data preprocessing technique and that it can improve the performance of various classifiers on multiclass imbalanced datasets. It has led us to the introduction of a new resampling algorithm, called Similarity Oversampling and Undersampling Preprocessing (SOUP), which resamples examples according to their difficulty. Its experimental evaluation on real and artificial datasets has shown that it is competitive with the most popular decomposition ensembles and better than specialized preprocessing techniques for multi-imbalanced problems.

Estimation of the ischemic brain temperature with the particle filter method

Nunes Felipe S., Orlande Helcio R. B., Nowak Andrzej J.

Computer Assisted Methods in Engineering and Science

2019

Vol. 26, no. 1

5--19

In this work, a two-dimensional model was developed to analyze the transient temperature distribution in the head of a newborn human, during local cooling promoted by the flow of cold water through a cap. The inverse problem dealt with the sequential estimation of the internal temperature of the head, by performing non-invasive transient temperature measurements. A state estimation problem was solved with the sampling importance resampling (SIR) algorithm of the particle filter method. Uncertainties in the evolution and observation models were assumed as additive, Gaussian, uncorrelated and with zero means. The uncertainties for the evolution model were obtained from the Monte Carlo simulations, based on the uncertainties of the model parameters. The head temperature was accurately predicted with the particle filter method. Such a technique might be applied in the future to monitor the brain temperature of newborns and control the local cooling treatment of neonatal hypoxic-ischemic encephalopathy.