The selection of gene identifier from microarray databases is a challenging task since microarray contains large number of gene attributes for a few samples. This article proposes a novel fuzzy-rough set-based gene expression features selection using fuzzy-rough reduct under multi-granular space for human diabetes patient. Firstly, fuzzy multi-granular gain has been computed from the expression datasets via fuzzy entropy which reduces the dimension of the database. Thereafter, the features have been selected from microarray using the fuzzy rough reduct and information gain with respect to their expression patterns. To reduce the computational cost, a decision making scheme has been designed using a rough approximation of a fuzzy concept in the field of multi-granulation framework. Finally, we have recognized the association among the genomes that have expressively different expression patterns from controlled state to the diabetic state with respect to their impression using modified fuzzy-rough nearest neighbour classifier (FRNNC). Five standard diabetic microarray datasets have been considered to quantify the efficiency of the designed FRNNC model and are validated with F measure using diabetes gene expression NCBI database and it performs superior compared to existing methods.

从微阵列数据库中选择基因标识符是一项具有挑战性的任务，因为微阵列包含少量样本的大量基因属性。本文提出了一种新的基于模糊粗糙集的基因表达特征选择，在多粒度空间下使用模糊粗糙减少人类糖尿病患者。首先，通过模糊熵从表达式数据集中计算出模糊多粒度增益，从而降低了数据库的维数。此后，使用模糊粗略归约和关于其表达模式的信息增益从微阵列中选择特征。为了降低计算成本，使用多粒度框架领域中模糊概念的粗略近似设计了决策方案。最后，我们已经认识到基因组之间的关联，这些基因组从受控状态到糖尿病状态具有明显不同的表达模式，并且使用改进的模糊粗糙最近邻分类器 (FRNNC)。五个标准的糖尿病微阵列数据集被认为可以量化设计的 FRNNC 模型的效率，并通过F使用糖尿病基因表达 NCBI 数据库进行测量，与现有方法相比，它的性能更优。