# Enhanced Feature Selection via Hierarchical Concept Modeling
## 2 Related Works  
Feature selection methods have been used for a wide variety of applications [1; 2]. Such methods proceed to eliminate unfavorable features that might be noisy, redundant, or irrelevant and could penalize the performance of a classifier [2]. Feature selection thus contributes to a reduction in the dimensionality of data and a restriction of the inputs, which can contain missing values in one or several features [1; 4]. The various feature selection methods fall into three main categories: filters, wrappers, and embedded methods [2; 3; 4; 20]. The first category of filter methods selects the features by using weights that indicate correlations between each feature and a class. The largest weights are selected in rank order. The calculated weights may be based on information gain [1; 3; 20], chi-square [21; 22], or other saliency measures. The second category is wrapper methods that evaluate feature subsets by directly using a learning algorithm and selecting features based on their impact on the model's performance [3; 20]. While this appears reasonable as a direct approach, it tends to be too computationally expensive. Finally, embedded methods incorporate feature selection within the learning algorithm during the training process [1; 2; 3]. Currently, several studies have explored innovative methods to improve learning frameworks, particularly in meta-learning, robustness, and advanced classification strategies. For instance, [PERSON] et al. investigated the adaptability of model-agnostic meta-learning (MAML) in NLP applications, providing insights into optimizing task generalization [23]. Similarly, the robustness of learning frameworks was examined in adversarial scenarios by [PERSON] et al., emphasizing the importance of reliability in sensitive domains like healthcare [24]. Additionally, [PERSON] et al. introduced hypergraph-enhanced semi-supervised classification, highlighting the utility of complex relational modeling [25]. These works provide valuable insights that contextualize and contrast our hierarchical feature selection approach. In prior studies, many feature selection methods were developed to identify the most relevant and informative features from a dataset. Selecting the right set of features can improve model performance, reduce overfitting, enhance interpretability, and speed up the learning process. This study focuses on applying a hierarchical concept model to select the features from available data. In recent years, feature selection based on a hierarchical information structure has been proposed because of its rational learning inside the structure. [PERSON] et al. [26] proposed a hierarchical feature selection framework by considering the parent-child relationship, sibling relationship, and family relationship. These relationships were modeled and implemented using a data matrix concept [5]. [PERSON] et al. [13] presented the hierarchical feature selection with subtree-based graph regularization by exploring two-way dependence among different classes. [PERSON] et al. [5] designed a feature selection strategy for hierarchical classification based on fuzzy rough sets to compute the lower and upper approximations of classes organized in a class hierarchy. The authors developed an efficient hierarchical feature selection algorithm based on sibling nodes [5]. [PERSON] et al. [27] proposed a feature selection framework based on semantic and structural information of labels for hierarchical feature selection by transforming to semantic regularization and adapted the proposed model to a directed acyclic graph case. [PERSON] et al. [28] proposed a robust hierarchical feature to reduce the adverse effects of data outliers and learn relatively robust and discriminative feature subsets for hierarchical classification. Hierarchical feature selection has gained attention due to its effectiveness in managing structured data relationships and optimizing feature sets within multi-level classification tasks.