## 5 Discussion and Conclusions  
To the best of our knowledge, only a limited number of studies have introduced methods for group feature ranking and selection, specifically for high-dimensional datasets. Previous studies have not used permutation methods for this purpose. In this study, a novel permutation-based approach was proposed that systematically computes the relative importance scores for all group features and subsequently ranks them based on their respective scores. The proposed method selects a subset comprising only the most important group features. To assess the efficacy of the proposed group feature ranking and selection method, rigorous evaluations were conducted using high-dimensional real-world datasets. The datasets analyzed in this study demonstrated a mix of high dimensionality and a limited number of samples. With feature numbers extending to thousands and sample sizes remaining in the range of a few hundred, a practical approach to dimensionality reduction is imperative. This study emphasized the importance of a careful reduction strategy. This highlights the fact that datasets with moderate sizes or dimensions do not necessarily require drastic dimensionality reduction. For practitioners working with datasets of different sizes, adjusting the parameters at the beginning allows for fine-tuning the removal of irrelevant features according to specific needs. Notably, this study showed a performance improvement when the dimensionality reduction was more aggressive in the early stages. The introduced methodology enhances the interpretability of group features, an aspect that existing methods weaken by not providing information regarding the relative importance of selected groups. Unlike existing group feature selection methods, such as the group lasso, which cannot describe the specific contributions or importance of selected or rejected groups, our proposed method provides insights into both selected and rejected groups. Moreover, the proposed method maintains a competitive classification performance in the field of machine learning algorithms, positioning itself at an equal level with existing methodologies. This paper introduced a group feature selection method that employs a permutation-based strategy specifically designed for datasets characterized by high dimensionality and small sample sizes. Initial dimensionality reduction was accomplished by eliminating irrelevant individual features within each group, followed by the computation of the relative importance of all groups. Subsequently, groups with importance values exceeding the average were selected. The data obtained from these groups were divided into training and testing datasets. Machine learning algorithms were trained on the training data, and model performance was evaluated using test data. Given the small sample size of the datasets used in this study, leave-one-out cross-validation was employed to assess the performance of the proposed method. The experimental results demonstrated that the performance of the proposed method was comparable or superior to that of existing methodologies in machine learning algorithms. The proposed method exhibited distinctive characteristics that are particularly noteworthy in the context of high-dimensional datasets. Notably, the method undertook an aggressive dimensionality reduction, yielding a higher classification accuracy than that of the original datasets, despite the removal of a substantial number of features. A notable aspect of the proposed method is its departure from existing permutation importance methods, which are typically applied to compute the importance of individual features. Existing methods tend to overestimate the importance of specific features in scenarios characterized by feature correlations and group structures. To address this, the proposed method employs permutations at the group level, offering a more accurate reflection of the importance of group features within the context of correlated features and group structures.