## 2 Materials and Methods
### Related Works  
The studies described have a relationship with the study undertaken in feature selection. Similar to reference research by [PERSON] et al. [8], this study presents a novel framework for selecting Hybrid Ensemble Feature Selection (HEFS) features. The existing characteristics are then eliminated using HEFS, leaving ten baseline features. A test involving ten baseline features showed that random forest outperformed the accuracy of the other five machine learning algorithms, which was 94.6%. The subsequent study was conducted by [PERSON] and [PERSON] [35]. They proposed a classification model based on heuristic features taken from URLs, source code, and third-party services obtained from the analysis with eight machine learning algorithms. Random forest outperformed the accuracy value by 99.31% of the first experiment comparing eight machine learning methods. In the second experiment comparing principal component analysis (PCA-RF) with oblique Random Forests (oRFs), PCA-RF had a higher accuracy rating of 99.551%. [PERSON] et al. [36] utilized the UCI phishing dataset to test the list of spam and phishing using six machine learning algorithms. They then conducted three software experiments: the first without changing the dataset, the second with BestFirst + CfsSubSEvaluation, and the third with Ranker + PrincipalComponents Optimization, WEKA. Random forest outperformed other machine learning algorithms on the first, second, and third trials with phishing accuracy scores of 97.26%, 94.77%, and 97.33%, respectively. [PERSON] et al. [9] researched the detection of phishing websites using random forests. Based on the results of testing seven algorithms, they discovered that only one algorithm, Random Forest combined with NLP, produced the best results, achieving an accuracy rate of 97.98%. [PERSON] et al. [37] employed five supervised machine learning methods, including NB, DT, RF, SVM, and MLP, on a 100,315-dataset. The features utilized are a combination of ten features that are typically employed in this type of study and the two new features proposed by the authors. The hyperparameter tuning approach yields just three of the four features, namely RF, SVM, and MLP, with great performance values. [PERSON] & [PERSON] [11] presented two approaches for selecting and eliminating features: consensus and majority voting. They use two datasets to evaluate the method. Consensus provided 17 features from the first dataset, while most votes yielded 23 features. For the second dataset, consensus yields three features, while majority voting yields thirteen. Voting is the approach with the highest accuracy, with a value of 98.63%. [PERSON] et al. [20] conducted similar research utilizing Artificial Neural Network (ANN), DT, Gaussian NB, Gradient Boosting (GB), KNN, RF, SVC, and SV. They also used three feature selection strategies, including URL and HTML based, URL based only, Transformer (BERT, ELECTTRA) NLP. The BERT and ELECTRA methods using ANN outperform others. Previous similar research was undertaken by [PERSON] et al. (2019). They use a genetic algorithm (GA) to optimize the parameters of several ensemble machine learning algorithms, such as XGBoost, LightGBM, random forests, AdaBoost, GB, and Bagging. The optimized classifiers were then ranked, and the three top models were selected as the foundational classifiers for a stacking ensemble technique. The trials were conducted using two datasets of phishing websites. In the first dataset, the stacking ensemble approach yields an accuracy value of 97.16%, whereas, in the second dataset, the accuracy value is 98.50%. Table 2 provides a brief description of the associated works. The research listed is related to the work done on feature selection and machine learning methods. According to the researchers' findings, the machine learning algorithm that outperforms others is RF ([PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2020). As a result, the method employed in this work is the RF algorithm.