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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Hybrid Ant Bee Algorithm for Fuzzy Expert System Based Sample Classification
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Hybrid Ant Bee Algorithm for Fuzzy Expert System Based Sample Classification

机译：基于样本分类的模糊蚁群混合蚂蚁算法

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摘要

Accuracy maximization and complexity minimization are the two main goals of a fuzzy expert system based microarray data classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand. To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is treated as a combinatorial optimization task. Ant colony optimization (ACO) with local and global pheromone updations are applied to find out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous expression values of a gene, this paper employs artificial bee colony (ABC) algorithm to evolve the points of membership function. Mutual Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with highly interpretable and compact rules for all the data sets when compared with other approaches.
机译：精度最大化和复杂度最小化是基于模糊专家系统的微阵列数据分类的两个主要目标。我们以前的遗传群算法（GSA）方法以其可解释性为代价，提高了模糊专家系统的分类精度。由GSA制定的if-then规则冗长而复杂，这使医生难以理解。为了解决这种可解释性-准确性的折衷，使用整数表示规则集，并将规则生成的任务视为组合优化任务。应用具有局部和全局信息素更新的蚁群优化（ACO），基于基因表达值找出模糊分区，以生成更简单的规则集。为了解决一个基因的无形式和连续表达值，本文采用人工蜂群（ABC）算法来演化隶属函数的点。相互信息用于识别信息基因。使用六个基因表达数据集评估了提出的混合蚂蚁蜂算法（ABA）的性能。从仿真研究中发现，与其他方法相比，该方法为所有数据集生成了一个具有高度可解释性和紧凑性规则的精确模糊系统。

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