## 2 Related Work
### Individual Feature Ranking and Selection  
Feature ranking and selection are crucial areas in machine learning and data mining [15]. Numerous techniques have been developed to address this problem. These methodologies have been widely applied in various real-world domains, including microarray gene analysis, text recognition, malware detection, image processing, image retrieval, and information retrieval [16; 17; 18; 19; 20]. Feature ranking and selection methods can be classified into two primary groups: subset methods and individual evaluation methods. Subset evaluation methods involve the selection of a subset of features for model construction using a search strategy. Conversely, individual evaluation methods measure the relevance of each feature to the target variable, assigning importance scores or ranks based on their correlation with the target variable [21]. Furthermore, these methods fall under the categories of filter, wrapper, hybrid, and embedded techniques [22; 23; 24; 25]. The primary category, known as the filter method [22], examines features based on their mathematical and statistical attributes during selection. Notably, this method operates independently of classifiers in decision-making, leading to rapid processing. This makes it effective for handling high-dimensional datasets. The second category, the wrapper method [23], employs the selection of an optimal subset of individual features and subsequently evaluates the subset's goodness with the help of a classifier. This method is renowned for its superior classification performance [4] compared to filter methods. How  
Figure 1: Flow chart of the proposed methodology. ever, it is not recommended for use with high-dimensional datasets [3], due to intensive computational demands. The hybrid methods, which constitute the third category [24], combine the advantages of both the filter and wrapper methods. A hybrid method creates a trade-off between the computational efficiency of the filter method and the superior classification ability of the wrapper method. Recognized for its robustness in feature selection and enhanced classification performance, the hybrid method stands out as an intelligent compromise. The final category, the embedded method [25], is a feature selection approach that combines model training and feature selection simultaneously. The embedded method employs a classifier to select the most relevant features as it trains [22]. Most feature ranking methods belong to the filter category. These methods deploy diverse filtering strategies to evaluate how related the features are to the target variable. Some notable strategies include the following: The chi-squared statistic (\(X^{2}\)) is a commonly used method [26] for feature ranking. This method evaluates the importance of an individual feature by computing chi-square statistics with respect to the class. Information gain (IG) plays a crucial role in an array of techniques for ranking and selecting features [27]. This metric measures the efficacy of a feature in classifying datasets by computing the decrease in dataset entropy, which represents a measure of uncertainty within a dataset, after splitting the dataset based on the feature. Notably, IG does not have any problem with features that have samples with large values. However, IG is biased toward features that have a large number of different values [28]. A gain ratio is introduced to address this bias. The gain ratio (GR) is a nonsymmetrical measure crucial for individual feature evaluation [29]. This approach is frequently used to address the biases inherent in the IG, which tend to exhibit a preference for features with a multitude of distinct possible values. Relief, introduced by [PERSON] and [PERSON] [30], is another important method for feature ranking. It employs a distance metric to calculate the importance or ranking of features. Relief is an extension of the original Relief algorithm that is adapted for noisy, incomplete datasets or those with multiple classes [31].