Novel swarm optimization for mining classification rules on thyroid gland data
Novel swarm optimization for mining classification rules on thyroid gland data
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DOI：
10.1016/j.ins.2012.02.009
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发表时间：
2012-08-15
影响因子：
8.1
通讯作者：
Yeh, Wei-Chang
中科院分区：
文献类型：
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作者：
Yeh, Wei-Chang
本文采用一种新的基于规则的分类器设计方法，利用改进的简化群优化算法（SSO），从UCI数据库中挖掘甲状腺数据集。该方法引入了精英概念以提高解的质量，引入了紧密区间编码（CIE）以有效地表示规则结构，引入了正交阵列测试（OAT）以有效地修剪规则以避免过度拟合训练数据集.为了评估所提出的改进SSO的分类性能，计算机模拟进行众所周知的甲状腺数据。计算结果与使用现有的算法，如传统的分类器，包括贝叶斯分类器，k-NN，k-Means，和2D-SOM，和软计算为基础的方法，如简单的SSO，免疫估计的分布算法（IEDA），和遗传算法（GA）获得的那些相比毫不逊色。(C)2012 Elsevier Inc. All rights reserved.
This work uses a novel rule-based classifier design method, constructed by using improved simplified swarm optimization (SSO), to mine a thyroid gland dataset from UCI databases. An elite concept is added to the proposed method to improve solution quality, close interval encoding (CIE) is added to efficiently represent the rule structure, and the orthogonal array test (OAT) is added to powerfully prune rules to avoid over-fitting the training dataset. To evaluate the classification performance of the proposed improved SSO, computer simulations are performed on well-known thyroid gland data. Computational results compare favorably with those obtained using existing algorithms such as conventional classifiers, including Bayes classifier, k-NN, k-Means, and 2D-SOM, and soft computing based methods such as the simple SSO, immune-estimation of distribution algorithms (IEDA), and genetic algorithm (GA). (C) 2012 Elsevier Inc. All rights reserved.