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Fast imbalanced classification of healthcare data with missing values | IEEE Conference Publication | IEEE Xplore

Fast imbalanced classification of healthcare data with missing values


Abstract:

In medical domain, data features often contain missing values. This can create serious bias in the predictive modeling. Typical standard data mining methods often produce...Show More

Abstract:

In medical domain, data features often contain missing values. This can create serious bias in the predictive modeling. Typical standard data mining methods often produce poor performance measures. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. The proposed method is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results.
Date of Conference: 06-09 July 2015
Date Added to IEEE Xplore: 17 September 2015
ISBN Information:
Conference Location: Washington, DC, USA

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