Over- and Under-sampling Approach for Extremely Imbalanced and Small Minority Data Problem in Health Record Analysis

Frontiers in Public Health
Koichi FujiwaraManabu Kano

Abstract

A considerable amount of health record (HR) data has been stored due to recent advances in the digitalization of medical systems. However, it is not always easy to analyze HR data, particularly when the number of persons with a target disease is too small in comparison with the population. This situation is called the imbalanced data problem. Over-sampling and under-sampling are two approaches for redressing an imbalance between minority and majority examples, which can be combined into ensemble algorithms. However, these approaches do not function when the absolute number of minority examples is small, which is called the extremely imbalanced and small minority (EISM) data problem. The present work proposes a new algorithm called boosting combined with heuristic under-sampling and distribution-based sampling (HUSDOS-Boost) to solve the EISM data problem. To make an artificially balanced dataset from the original imbalanced datasets, HUSDOS-Boost uses both under-sampling and over-sampling to eliminate redundant majority examples based on prior boosting results and to generate artificial minority examples by following the minority class distribution. The performance and characteristics of HUSDOS-Boost were evaluated through appl...Continue Reading

References

May 29, 1999·Statistical Methods in Medical Research·J Barnard, X L Meng
Feb 3, 2015·Journal of Bone and Mineral Research : the Official Journal of the American Society for Bone and Mineral Research·Anne L SchaferDeborah E Sellmeyer
Feb 4, 2016·IEEE Transactions on Bio-medical Engineering·Koichi FujiwaraEisuke Matsushima
Jun 24, 2016·IEEE Transactions on Cybernetics·Pin LimKay Chen Tan
Oct 16, 2016·IEEE Transactions on Bio-medical Engineering·Po-Yen WuMay D Wang

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Software Mentioned

CART
RUSBOOST
Windows
MICE
R
AUPRC
DOS
HUS
Boost
ADASYN

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