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Multi-modal evolutionary ensemble classification in medical diagnosis problems

机译：医学诊断问题的多模态进化集合分类

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Expert systems for classification tasks in medical diagnosis systems require two properties. The true positives should be very high, as well as the true negatives, i.e. the system should correctly catch those who are ill, and correctly dismiss those who are healthy. The multi-modal evolutionary classifier uses a genetic algorithm to learn a reference vector for each class, and classification is done by measuring the distance of the new example to reference vectors. For complex datasets such as medical diagnosis, interactions between features are typically complex and the multi-modal classifier's single reference vector is not able to capture this. In this work an extension to the algorithm is proposed, which learn sets of multi-modal classifiers using resampling and form an ensemble from these, using a genetic algorithm. The algorithm is evaluated on a sample of publicly available medical diagnosis datasets. While this is a work-in-progress, initial findings are that compared to the base classifier, using evolutionary learned ensembles improves accuracy in all cases, and is a direction for future work.
机译：医学诊断系统中分类任务的专家系统需要两个属性。真正的积极因素应该非常高，以及真正的否定，即，系统应该正确抓住那些生病的人，并正确地解雇那些健康的人。多模态进化分类器使用遗传算法来学习每个类的参考矢量，并且通过测量新示例的距离来完成对参考向量的分类。对于诸如医学诊断的复杂数据集，特征之间的交互通常是复杂的，并且多模态分类器的单个参考向量无法捕获此。在这项工作中，提出了算法的扩展，该扩展使用遗传算法学习使用重采样的重采样和从这些组合的多模态分类器组。该算法在公共医疗诊断数据集的样本上进行评估。虽然这是一项过程，但初始发现与基本分类器相比，使用进化学习集合可以提高所有情况的准确性，并且是未来工作的方向。

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