## 1 Introduction  
In pursuit of enhancing the effectiveness, robustness, and feasibility of predictive models, the analysis of relevant features has become a necessary task in machine learning and data mining projects. The benefits of feature selection are several [1]: ameliorating the prediction performance of the predictors, producing speedier and more cost-effective predictors, and presenting a better interpretability context of the generated data [2], or avoidance of curse of dimensionality [3, 4].  
Selection techniques can be divided according to the contribution of their application. Some cover the dimensionality of the original data set with Feature Extraction techniques like Principal Component Analysis [5], t-Distributed Stochastic Neighbor Embedding [6], or aggregation models [7]. Others focus on creating a feature ranking [8] to select the most influential features, through methods such as Feature Importance from Tree-Based Models [9], Recursive Feature Elimination (RFE) [10], Mutual Information [11], Correlation-Based Feature Ranking [12], between others. Another feature selection approach focuses on creating new variables from simple combinations such as polynomial features [13].  
The feature selection process can also be classified as a filter [14], wrapper [15], or embedded [16] method according to the implementation of variable elimination procedure within the data mining process [1]. Filter method, popular for high feature quantities, is independent of the model, while the embedded method selects features during model building and wrapper techniques use a predictive model to select the features.  
The selection of a specific technique depends strongly on the predictive objective and the nature of the data. Recent studies have shown that in certain cases, there may be combinations of features that are similarly efficient and that techniques such as RFE are not efficient [17]. In the case of highly correlated features, the inefficacity of standard RFE might be similar [18], but the combination of distinct strategies can improve the efficiency of standard techniques, as in [2], where a hybrid RFE strategy provide better results than single RFE methods.  
Another important part of the data mining is the interpretability of the resultant feature selection. Comprehensible and useful information can be extracted from feature combinations, and the relationships between feature entities can be explored through distinct connection criteria, such as association rule mining [19], also known as Market Basket Analysis techniques [20]. These practices are popular to identify changing trends of market data [21; 22], but also to reduce the dimensionality of input parameters [23].  
In what follows, a wrapper-kind methodology is presented in order to extract a representative feature group from feature association trends. For validation purposes, the proposed strategy is applied to a data set already mentioned in [17], as detailed in Section 2. Secondly, the definition of two distinct association metrics introduces the principal comparison of this research in Section 2.2.1. Subsequently, selected graph analysis techniques are described in order to distinguish visually the effect of those association rules. Straightaway, a section is dedicated to scrutinizing the validation procedures. Finally, all numerical and visual results are displayed in comparative tables and figures. The main contribution is the highlighted subgroup of combination with a common feature core, and a final two feature collections derived from both distinct association metrics.