Gene selection for cancer tumor detection using a novel memetic algorithm with a multi-view fitness function

计算机科学 模因算法 可解释性 模糊规则 适应度函数 模糊逻辑 人工智能 机器学习 分类器(UML) 数据挖掘 进化算法 算法 模糊集 遗传算法
作者
A. Zibakhsh,Mohammad Saniee Abadeh
出处
期刊:Engineering Applications of Artificial Intelligence [Elsevier BV]
卷期号:26 (4): 1274-1281 被引量:47
标识
DOI:10.1016/j.engappai.2012.12.009
摘要

Cancer is one of the key research topics in the medical field. An accurate detection of different cancer tumor types has great value in providing better treatment facilities and risk minimization for patients. Recently, DNA microarray-based gene expression profiles have been employed to correlate the clinical behavior of cancers with the differential gene expression levels in cancerous and benign tumors. 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 well-known and frequently used techniques for designing classifiers from microarray data, such as a support vector machine, neural networks, k-nearest neighbor, and logistic regression model, suffer from low comprehensibility. This paper proposes a new memetic algorithm which is capable of extracting interpretable and accurate fuzzy if–then rules from cancer data. This paper is the first proposal of memetic algorithms with the Multi-View fitness function approach. The new presented Multi-View fitness function considers two kinds of evaluating procedures. The first procedure, which is located in the main evolutionary structure of the algorithm, evaluates each single fuzzy if–then rule according to the specified rule quality (the evaluating procedure does not consider other rules). However, the second procedure determines the quality of each fuzzy rule according to the whole fuzzy rule set performance. In comparison to classic memetic algorithms, these kinds of memetic algorithms enhance the rule discovery process significantly.
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