Hybrid gene selection method based on mutual information technique and dragonfly optimization algorithm

One of the most prevalent problems with big data is that many of the features are irrelevant. Gene selection has been shown to improve the outcomes of many algorithms, but it is a difficult task in microarray data mining because most microarray datasets have only a few hundred records but thousands of variables. This type of dataset increases the chances of discovering incorrect predictions due to chance. Finding the most relevant genes is generally the most difficult part of creating a reliable classification model. Irrelevant and duplicated attributes have a negative impact on categorization algorithms’ accuracy. Many Machine Learning-based Gene Selection methods have been explored in the literature, with the aim of improving dimensionality reduction precision. Gene selection is a technique for extracting the most relevant data from a series of datasets. The classification method, which can be used in machine learning, pattern recognition, and signal processing, will benefit from further developments in the Gene selection technique. The goal of the feature selection is to select the smallest subset of features but carrying as much information about the class as possible. This paper models the gene selection approach as a binary-based optimization algorithm in discrete space, which directs binary dragonfly optimization algorithm «BDA» and verifies it in a chosen fitness function utilizing precision of the dataset’s k-nearest neighbors’ classifier. The experimental results revealed that the proposed algorithm, dubbed MI-BDA, in terms of precision of results as measured by cost of calculations and classification accuracy, it outperforms other algorithm