Group feature selection methods select the important group features by removing the irrelevant group features for reducing the complexity of the model. There are a few group feature selection methods that used ranking techniques. Ranking methods provide the relative importance of each group. For this purpose, we developed a sparse group feature ranking method based on the dimension reduction technique for high dimensional data. Firstly, we applied relief to each group to remove irrelevant features. Secondly, we extract the new feature that represents each feature group. To this end, we reduce the dimension of the group feature into a single dimension by applying Fisher linear discriminant analysis (FDA) for each feature group. At last, we estimate the relative importance of the new feature by applying random forest and selecting those important features that have higher importance compared with other ones. In the end, different machine-learning algorithms were used to train and test the models. For the experiment, we compared the proposed with supervised group lasso (SGL) methods by using real-life high-dimensional datasets. Results show that the proposed method selects a few important group features just like the existing group feature selection method and provides the ranking and relative importance of all group features. SGL slightly performs better on logistic regression whereas the proposed method performs better on support vector machine, random forest, and gradient boosting in terms of classification performance metrics.

The selection of group features is a critical aspect in reducing model complexity by choosing the most essential group features, while eliminating the less significant ones. The existing group feature selection methods select a set of important group features, without providing the relative importance of all group features. Moreover, few methods consider the relative importance of group features in the selection process. This study introduces a permutation-based group feature selection approach specifically designed for high-dimensional multiclass datasets. Initially, the least absolute shrinkage and selection operator (lasso) method was applied to eliminate irrelevant individual features within each group feature. Subsequently, the relative importance of the group features was computed using a random-forest-based permutation method. Accordingly, the process selected the highly significant group features. The performance of the proposed method was evaluated using machine learning algorithms and compared with the performance of other approaches, such as group lasso. We used real-world, high-dimensional, multiclass microarray datasets to demonstrate its effectiveness. The results highlighted the capability of the proposed method, which not only selected significant group features but also provided the relative importance and ranking of all group features. Furthermore, the proposed method outperformed the existing method in terms of accuracy and F1 score.

One of the main challenges for leading manufacturing industries to retain their competitiveness is selection of appropriate suppliers. In developing countries like Pakistan, industries need reinforcement of the supplier selection decisions using scientific techniques. This study utilizes conjoint analysis (CA), a mathematical and statistical modeling based multi-criteria decision making (MCDM) methodology, for evaluation of supplier efficiency in a real world manufacturing organization. Six attributes are extracted from respective literature with the help of expert opinions. The developed mathematical models are solved in respective CA software by using actual data. Results reveal that product performance is the most important attribute in supplier evaluation for the concerned industry followed by costs, quality, financial stability, delivery time and technical ability respectively. The findings are beneficial for both the selected manufacturing industry and the market suppliers.