## 3 Results and Discussion  
Initially, it is important to mention that in this research, a dataset was formed containing the reflectance values of the 380 spectral bands of 96 asbestos--cement (AC) roofs, which have been classified through field analysis as LIP and HIP (1 or 0). Thus, in Figure 3, a summary of the considered dataset is presented, loaded through the Python pandas library, where the reflectance values normalized to the dimensionless interval [0, 1] are displayed in 380 columns (0 to 379), along with the 'class' column, labeling each AC roof as LIP (1) or HIP (0). Specifically, in Figure 4, and according to the standardization shown in Figure 3, it is possible to separately observe the 48 signatures that were labeled in the dataset as LIP (Figure 4a), as well as the 48 spectral signatures corresponding to the roofs labeled as HIP (Figure 4b). Thus, even though the spectral signatures are of the same material, the classification models should have the capability to identify significant differences in reflectance values that enable the differentiation of the two types of AC roofs. Figure 3: Normalized asbestos--cement roofs dataset. Starting from the previously presented dataset, the identification of spectral bands in the spectrum that are most relevant in detecting AC roofs with LIP and HIP was undertaken. In this context, attribute selection in the field of machine learning significantly improves the computational efficiency of the model by reducing the number of features to consider during training and prediction. Similarly, focusing solely on the most relevant reflectance bands allows for model simplification, decreasing its complexity and facilitating interpretation. It is worth noting that complexity reduction not only enhances computational efficiency but also serves as an essential mechanism to prevent overfitting, enabling the model to generalize more effectively to unseen data and maintaining robust performance in detecting AC roofs under various conditions. Thus, for the selection of the best attributes or bands influencing the classification of roofs as LIP or HIP, an attribute evaluation method based on information gain was employed. The mentioned method can be applied through the functionalities provided via the Weka data mining tool, which is known as InfoGainAttributeEval and operates in conjunction with the Ranker search method (see Figure 5). InfoGainAttributeEval utilizes information gain, which is a concept from information theory that quantifies the ability of an attribute to enhance the predictability of the model. In combination with the Ranker search method, which assigns a score to each attribute based on its merit according to the evaluation via InfoGainAttributeEval, it is possible to obtain the attributes of the dataset ranked in order of their relevance. Thus, this approach enables the classification of attributes based on their contribution to information, facilitating the selection of a set of relevant attributes to improve computational efficiency, reduce model complexity, and mitigate the risk of overfitting. In this way, the InfoGainAttributeEval method combined with Ranker offers a significant competitive advantage over other methods such as Correlation-based Feature Selection and Recursive Feature Elimination due to its ability to generate an accurate ranking of the dataset attributes, organized by their merit score. This combination efficiently and directly identifies the most informative features, thereby optimizing variable selection and enhancing model interpretability, resulting in a more robust and efficient modeling process. In Figure 6, the relevance values obtained for each attribute of the dataset via the InfoGainAttributeEval method through the Weka tool are presented. In Figure 6, it can be observed that the highest values in terms of the merit metric, which is presented in dimensionless normalized values in the interval [0 1], are found between bands 104 and 110 and between bands 218 and 225, with values close to 0.4.