## 1 Introduction
### Summation  
This study focuses on the preparation and analysis of bainitic microstructure data using a superpixel-based algorithm. The proposed approach aims to enhance the segmentation and feature extraction processes, allowing for a more precise characterization of the bainitic phase.  
Following data preparation, two classification techniques will be applied: the K-Nearest Neighbors (KNN) algorithm and tree-based methods. These techniques will be evaluated in terms of their effectiveness in distinguishing different bainitic structures.  
From tree-based methods the main focus has been set up on the following:  
* Decision trees (DTs) [16] are graphical depictions of rules derived from analyzing data structures. Their key advantages include their graphical representation, clarity, ease of interpretation and validation based on domain knowledge, the ability to assess predictor importance, resilience to noise and outliers, and the generation of rule sets that can be utilized in other applications. A decision tree is a graphical decision-support tool, organized as a tree structure where internal nodes test attribute values, and leaves represent classification decisions. Essentially, a decision tree consists of a series of conditional instructions. Decision trees are a sophisticated form of knowledge representation offering diverse interpretative possibilities, both during knowledge acquisition (data mining) and application in decision-making. They are widely used in industrial and research contexts [17].
* Random forest (RF) [18], also known as Bagged Decision Trees, is a method grounded in decision tree induction principles [19]. It constructs complex models by combining multiple decision trees into a single classification framework. Each tree calculates a value for the input, and the final result is obtained by averaging these outputs. This ensemble approach effectively mitigates the impact of outliers, a significant limitation of individual decision trees. A random forest consists of an ensemble of weak learners that, together, can address more complex classification problems. The classifier begins by randomizing the training data into multiple subsets. Each subset is used to develop a tailored classification tree model. Individual tree performance is assessed by evaluating attribute significance as predictors, using measures such as data impurity or entropy. Attributes with the highest scores are selected to split the data. By aggregating the outcomes of these individual trees, the random forest builds a robust model capable of handling diverse classification tasks.  
Tree-based methods allow us to easily test hypotheses without spending effort on sophisticated and computationally costly methods like neural networks.  
In the Section 5, the potential use of Convolutional Neural Networks (CNNs) [20] for automatic feature extraction will be considered as a direction for future work. Given their ability to learn relevant representations from image data, CNNs may provide an alternative to traditional knowledge-driven approaches.  
The final stage of this study involves a comprehensive analysis of the obtained results, followed by a discussion of the findings and conclusions regarding the performance of the applied methods.