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Support Vector Machine Classification of Uncertain and Imbalanced data using Robust Optimization

机译：使用稳健优化的不确定和不平衡数据的支持向量机分类

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摘要

In this paper, we have developed a robust Support Vector Machines (SVM) scheme of classifying imbalanced and noisy data using the principles of Robust Optimization. Uncertainty is prevalent in almost all datasets and has not been addressed efficiently by most data mining techniques, as these are based on deterministic mathematical tools. Imbalanced datasets exist while performing analysis of rare events, and for such datasets elements in the minority class become critical. Our method tries to address both issues lacking in traditional SVM classifications. At present, we provide solutions for linear classification of data having bounded uncertainties. This can be extended to non-linear classification schemes for any types of uncertainties that are convex. Our results in predicting the importance of the minority class are better than the traditional SVM soft-margin classification. Preliminary computational results are presented.
机译：在本文中，我们开发了一种鲁棒的支持向量机（SVM）方案，该方案使用鲁棒优化原理对不平衡和嘈杂的数据进行分类。不确定性在几乎所有数据集中都很普遍，大多数数据挖掘技术并不能有效地解决，因为这些技术都是基于确定性数学工具的。在执行稀有事件的分析时，存在不平衡的数据集，对于此类数据集，少数类中的元素变得至关重要。我们的方法试图解决传统SVM分类中缺少的两个问题。目前，我们提供了对具有有限不确定性的数据进行线性分类的解决方案。对于任何凸的不确定性类型，都可以将其扩展到非线性分类方案。我们预测少数派类别重要性的结果要优于传统的SVM软边际分类。给出了初步的计算结果。

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