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首页> 外文期刊>International Journal of Innovative Computing Information and Control >ACO BASED DISCOVERY OF COMPREHENSIBLE AND ACCURATE RULES FROM MEDICAL DATASETS
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ACO BASED DISCOVERY OF COMPREHENSIBLE AND ACCURATE RULES FROM MEDICAL DATASETS

机译：基于ACO的医疗数据集全面而准确的规则发现

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摘要

In many real world applications, comprehensibility of the classifier is as important as its accuracy. The medical field is one of those where this requirement is more pronounced. It is not enough for users in this field to have an accurate classifier, and they also need to verify and analyze the logic of the classification process. It is difficult to have confidence in a black box type of classifier when the classification decision is a matter of life and death of a patient. In recent years, algorithms for classification rule discovery based on the ant colony optimization meta-heuristic (ACO) have been proposed, which fulfill both the requirements of high accuracy and comprehensibility. This paper reports some improvements in a recently proposed ACO based classification algorithm, called CAntMiner, whose main feature is a heuristic function based on the compatibility of pairs of attribute-values and class labels, and its application on medical datasets. We study the performance of the algorithm for twelve commonly used datasets and compare it with ten well known classification algorithms, three of which are ACO based. Experimental results show that the accuracy rate obtained by CAntMiner is better than that of the compared algorithms. We also discuss some other issues related to comprehensibility of the classifier building process.
机译：在许多实际应用中，分类器的可理解性与其准确性一样重要。医学领域是该要求更为明显的领域之一。仅该领域的用户拥有一个准确的分类器是不够的，他们还需要验证和分析分类过程的逻辑。当分类决定是患者的生死攸关时，很难对黑匣子分类器有信心。近年来，提出了一种基于蚁群优化元启发式算法的分类规则发现算法，该算法既满足高精度要求又具有可理解性。本文报告了最近提出的基于ACO的分类算法CAntMiner的一些改进，该分类算法的主要特征是基于属性值对和类别标签对的兼容性的启发式函数及其在医学数据集上的应用。我们研究了十二种常用数据集的算法性能，并将其与十种众所周知的分类算法进行了比较，其中三种基于ACO。实验结果表明，CAntMiner所获得的准确率要优于比较算法。我们还将讨论与分类器构建过程的可理解性有关的其他一些问题。

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