Models based on machine learning algorithms have been developed to detect the breast cancer disease early. Feature selection is commonly applied to improve the performance of these models through selecting only relevant features. However, selecting relevant features in unsupervised learning is much difficult. This is due to the absence of class labels that guide the search for relevant information. This kind of the problem has rarely been studied in the literature. This paper presents a hybrid intelligence model that uses the cluster analysis algorithms with bio-inspired algorithms as feature selection for analyzing clinical breast cancer data. A binary version of both moth flame optimization and whale optimization algorithm is proposed. Two evaluation criteria are adopted to evaluate the proposed algorithms: clustering-based measurements and statistics-based measurements. The experimental results positively demonstrate that the capability of the proposed bio-inspired feature selection algorithms to produce both meaningful data partitions and significant feature subsets.

中文翻译：

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用于临床乳腺癌诊断的二进制鲸鱼优化算法和带有聚类算法的二进制飞蛾火焰优化

已经开发出基于机器学习算法的模型来早期检测乳腺癌疾病。特征选择通常用于通过仅选择相关特征来提高这些模型的性能。然而，在无监督学习中选择相关特征非常困难。这是因为缺少指导搜索相关信息的类标签。这类问题在文献中鲜有研究。本文提出了一种混合智能模型，该模型使用聚类分析算法和仿生算法作为特征选择来分析临床乳腺癌数据。提出了蛾火焰优化和鲸鱼优化算法的二进制版本。采用两个评估标准来评估所提出的算法：基于聚类的测量和基于统计的测量。实验结果积极地证明了所提出的仿生特征选择算法能够产生有意义的数据分区和重要的特征子集。