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An Application Of A New Meta-heuristic For Optimizing The Classification Accuracy When Analyzing Some Medical Datasets

机译：一种新的元启发式算法在分析某些医学数据集时优化分类准确性的应用

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摘要

Medical data mining has recently become one of the most popular topics in the data mining community. This is due to the societal importance of the field and also the particular computational challenges posed in this domain of data mining. However, current medical data mining approaches oftentimes use identical costs or just ignore them for the different cases of classification errors. Thus, their outcome may be unexpected. This paper applies a new meta-heuristic approach, called the Homogeneity-Based Algorithm (or HBA), for optimizing the classification accuracy when analyzing some medical datasets. The HBA first expresses the objective as an optimization problem in terms of the error rates and the associated penalty costs. These costs may be dramatically different in medical applications as the implications of having a false-positive and a false-negative case may be tremendously different. When the HBA is combined with traditional classification algorithms, it enhances their prediction accuracy. It does so by using the concept of homogenous sets. Five medical datasets, obtained from the machine learning data repository at the University of California, Irvine (UCI), USA, were tested. Some computational results indicate that the HBA, when it is combined with traditional methods, can significantly outperform current stand-alone data mining approaches.
机译：医学数据挖掘最近已成为数据挖掘社区中最受欢迎的主题之一。这是由于该领域的社会重要性以及该数据挖掘领域中的特殊计算挑战所致。但是，当前的医学数据挖掘方法通常使用相同的成本，或者因分类错误的不同情况而忽略它们。因此，他们的结果可能出乎意料。本文采用一种称为基于均一性的算法（或HBA）的新元启发式方法来优化分析某些医疗数据集时的分类准确性。 HBA首先根据错误率和相关的惩罚成本将目标表示为优化问题。在医疗应用中，这些成本可能会有很大的不同，因为假阳性和假阴性病例的含义可能会大大不同。当HBA与传统分类算法结合使用时，可以提高其预测准确性。它通过使用同质集的概念来实现。测试了五个数据集，这些数据集是从美国加利福尼亚大学欧文分校（UCI）的机器学习数据库中获得的。一些计算结果表明，与传统方法结合使用时，HBA可以大大胜过当前的独立数据挖掘方法。

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