Ho Shinn-Ying, Hsieh Chih-Hung, Chen Hung-Ming, Huang Hui-Ling
Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.
Biosystems. 2006 Sep;85(3):165-76. doi: 10.1016/j.biosystems.2006.01.002. Epub 2006 Feb 21.
An accurate classifier with linguistic interpretability using a small number of relevant genes is beneficial to microarray data analysis and development of inexpensive diagnostic tests. Several frequently used techniques for designing classifiers of microarray data, such as support vector machine, neural networks, k-nearest neighbor, and logistic regression model, suffer from low interpretabilities. This paper proposes an interpretable gene expression classifier (named iGEC) with an accurate and compact fuzzy rule base for microarray data analysis. The design of iGEC has three objectives to be simultaneously optimized: maximal classification accuracy, minimal number of rules, and minimal number of used genes. An "intelligent" genetic algorithm IGA is used to efficiently solve the design problem with a large number of tuning parameters. The performance of iGEC is evaluated using eight commonly-used data sets. It is shown that iGEC has an accurate, concise, and interpretable rule base (1.1 rules per class) on average in terms of test classification accuracy (87.9%), rule number (3.9), and used gene number (5.0). Moreover, iGEC not only has better performance than the existing fuzzy rule-based classifier in terms of the above-mentioned objectives, but also is more accurate than some existing non-rule-based classifiers.
使用少量相关基因且具有语言可解释性的精确分类器，有利于微阵列数据分析以及开发低成本诊断测试。一些常用于设计微阵列数据分类器的技术，如支持向量机、神经网络、k近邻和逻辑回归模型，都存在可解释性低的问题。本文提出了一种用于微阵列数据分析的具有精确且紧凑模糊规则库的可解释基因表达分类器（名为iGEC）。iGEC的设计有三个需要同时优化的目标：最大分类准确率、最少规则数和最少使用基因数。一种“智能”遗传算法IGA被用于有效解决具有大量调优参数的设计问题。使用八个常用数据集对iGEC的性能进行了评估。结果表明，就测试分类准确率（87.9%）、规则数（3.9）和使用基因数（5.0）而言，iGEC平均具有精确、简洁且可解释的规则库（每个类别1.1条规则）。此外，iGEC不仅在上述目标方面比现有的基于模糊规则的分类器性能更好，而且比一些现有的非基于规则的分类器更准确。
