Binary Fish School Search applied to feature selection: Application to ICU readmissions
Binary Fish School Search applied to feature selection: Application to ICU readmissions
复制标题
二元鱼群搜索应用于特征选择：应用于 ICU 再入院
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
C. J. A. B. Filho
中科院分区：
文献类型：
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作者：
Joao A. G. Sargo;S. Vieira;J. Sousa;C. J. A. B. Filho
本文提出了一种新的特征选择方法制定的基础上的鱼群搜索（FSS）优化算法，旨在科普过早收敛。为了使用这种人口为基础的优化算法的特征选择问题，我们提出了使用二进制编码方案的内部机制的鱼群搜索，新兴的二进制鱼群搜索（BFSS）。建议的算法相结合，模糊建模的包装方法的特征选择（FS）和测试超过三个基准数据库。该混合提案被应用于ICU（重症监护室）再入院问题。该应用程序的目的是预测ICU患者出院后24至72小时内的再入院率。我们评估的性能指标和每个使用FS算法选择的功能的数量方面的实验结果。我们观察到，我们的建议可以正确地选择区分输入功能。
This paper proposes a novel feature selection approach formulated based on the Fish School Search (FSS) optimization algorithm, intended to cope with premature convergence. In order to use this population based optimization algorithm in feature selection problems, we propose the use of a binary encoding scheme for the internal mechanisms of the fish school search, emerging the binary fish school search (BFSS). The suggested algorithm was combined with fuzzy modeling in a wrapper approach for Feature Selection (FS) and tested over three benchmark databases. This hybrid proposal was applied to an ICU (Intensive Care Unit) readmission problem. The purpose of this application was to predict the readmission of ICU patients within 24 to 72 hours after being discharged. We assessed the experimental results in terms of performance measures and the number of features selected by each used FS algorithms. We observed that our proposal can correctly select the discriminating input features.