## 4 Experiment Setup
### Experimental Setup  
The computer used in this experiment has the following basic configuration: Windows 10 operating system, Intel(R) Core (TM) i7-8750 CPU @ 2.20 GHz, 8 GB RAM (Intel, Santa Clara, CA, USA). The experiment was conducted in Python 3.8. The training set and validation set were randomly partitioned with a ratio of 7:3.  
To evaluate the performance of FSNT in feature selection for classification, three real datasets were chosen to assess the accuracy of classification, while another three real datasets were selected to examine the regression performance. The six real datasets used in the study are as follows: Student-por, Student-mat, Online News Popularity, Student Archive, Superconductivity, and TCGA Info with Grade; Student-por and Student-mat encompass scores of secondary school students from two Portuguese schools, collected through questionnaires and provided by [27]. Student-por is employed for regression to predict students' final scores, which are continuous values, whereas Student-mat is utilized for classification to predict students' scores divided into five categories. The Online News Popularity dataset, sourced from the website (www.mashable.com, accessed on 22 June 2023) and made available by [28], focuses on regression and aims to predict news popularity. Student Archive, provided by [29], was developed as part of a project to identify at-risk students early in their academic journey using machine learning technology; the dataset encompasses three types of classification tasks (dropout, enrollment, and graduates). The Superconductivity dataset, obtained from [30], pertains to superconducting materials and is used for regression to predict the critical temperature. TCGA Info with Grade involves the 20 most frequently mutated genes and three clinical features from projects. The objective is to determine whether patients exhibit specific clinical and molecular/mutation characteristics of LGG or GBM, as provided by [31]. These datasets are derived from practical applications and are typically employed to compare the classification performance of feature selection results in subsequent classification learning models, thereby evaluating the algorithm. Refer to Tables 1 and 2 for basic information on these test datasets, including name, sample size, number of features, and number of categories.  
To address the issue of data imbalance and ensure sample balance, we employed the SMOTENC algorithm [32]. This algorithm is an enhanced version of the SMOTE oversampling algorithm, and is capable of handling both continuous and discrete data. The algorithm's workflow can be described as follows:  
1. For each sample \((x_{1},x_{2})\) belonging to a minority class label, calculate its distance from sample points of other minority class labels in the multidimensional space. Obtain  
Figure 1: Overall experimental process.  
the k nearest neighboring points to the sample by performing the K-nearest neighbors (KNN) algorithm on the sample points of the minority class label.
2. Determine the sampling rate based on the proportion of each sample label type. For sample points belonging to a minority class with a relatively small proportion compared to other labels, randomly select a subset of samples from their k neighboring points. These selected samples are denoted as \(\left(x_{1}^{{}^{\prime}},x_{2}^{{}^{\prime}}\right)\).
3. For continuous data, and each selected adjacent sample from the previous step, generate a new sample according to Equation (20): \[\left(X_{1},X_{2}\right)=\left(x_{1},x_{1}\right)+rand(0,1)\times\Delta\] (20) where \(rand(0,1)\) represents a random number between 0 and 1, and \(\Delta=\left\{\left(x_{1}^{{}^{\prime}}-x_{1}\right),\left(x_{2}^{{}^{\prime}} -x_{2}\right)\right\}\).
4. For discrete data, the new sample value is determined by selecting the discrete value with the highest frequency of occurrence among the nearest neighbor samples.