# Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model
## 1 Introduction  
The technology of DNA microarray assists in making it simpler to monitor a huge number of genes simultaneously [1]. Earlier works indicated that the technology of DNA microarray could be useful in the classification of cancer disease [2]. To classify microarray gene expression, several techniques and methods were introduced that have satisfactory outcomes [3]. For the microarray dataset, the gene expression value is organized through the matrix, where samples are rows and genes or features are columns. The value of gene expression is a real number, and it defines the expression level of a gene following certain criteria [4]. Due to the limited number of samples with an enormous number of features from the gene expression data, the systematic machine learning (ML) technique does not work well for cancer classifiers [5].  
A microarray experiment produces many gene expression data in an individual sample. The ratio of the number of genes (features) to the number of patients (samples) is skewed,leading to the popular curse-of-dimensionality problem [6]. Furthermore, it enforces self-inflicting limitations on the presenting of methods: (i) processing all the information may not be possible, and (ii) processing a set of data might lead to overfitting, local maxima, and loss of information. These two problems affect the reliability and accuracy of machine learning techniques. Several studies have been conducted to identify an effective feature set [7]. Statistical and evolutionary approaches were introduced for these purposes. Feature subset selection (FSS) methods such as joint mutual information (JMI), joint mutual information maximization (JMIM), and minimum redundancy maximum relevance (mRMR) are among the main statistical methods [8].  
Literature reviews showed that recent innovative technologies such as genetic algorithm (GA), mining techniques, transfer learning, deep neural network (DNN), particle swarm optimization (PSO), and so on, generate precise results [9]. The classification of microarray data is generally performed in two different ways. Feature selection (FS) focuses on choosing the most important characteristics from a large dataset to decrease computation overheads, overfitting, and noise. The classifier training process constructs a technique in the selected feature to accurately categorize a microarray sample. Innovative technologies such as convolutional neural network (CNN), image processing, ant miner, transfer learning, and experimental methods were introduced in a previous study [10]. Even though the innovative technologies for FS and classifier training can produce higher accuracy, they should be tuned based on the fundamental data set in a controlled setup to accomplish better outcomes.  
We developed a novel red fox optimizer with deep-learning-enabled microarray gene expression classification (RFODL-MGEC) model. The presented RFODL-MGEC model uses a novel RFO-based feature selection (FS) approach to derive an optimum subset of features. Moreover, the proposed RFODL-MGEC model involves a bidirectional cascaded deep neural network (BCDNN) for data classification. The parameters involved in the BCDNN method were optimally tuned using a chaos game optimization (CGO) algorithm.