## 2 Background
### Instance Reduction Techniques (IR)  
Instance Reduction Techniques (IR), sometimes referred to as Instance Selection Algorithms (IS), reduce the size of the training dataset by selecting which instances to keep for use in the generalization process [14]. Instance Reduction Techniques are most commonly utilized in the context of Instance-Based Learning (IBL) [26], although they are used to improve classification accuracy by removing noisy instances, speed up neural networks [27; 28], and reduce excessive memory storage [29].  
Instance Reduction Techniques aim to obtain a subset S from a training set, T, with the objective of excluding irrelevant instances that may be noisy or redundant. The resulting subset \(S\) should ideally preserve the overall characteristics of the original training set, ensuring that the accuracy of the learning algorithm on \(S\) (denoted as \(Accuracy(S)\)) is close to the accuracy on the full training set \(T\) (denoted as \(Accuracy(T)\)) [30].  
The purpose of these techniques is to reduce the size of the training set while minimizing the impact on performance. Interestingly, it has been observed that instance reduction techniques can even enhance the classification accuracy of neural networks and reduce training time by eliminating outliers or border instances, which in turn helps to smooth the decision boundaries prior to the model training stage [27; 28]. By removing these potentially misleading or noisy instances, the instance reduction techniques contribute to improving the overall quality of the training set, leading to more robust and accurate neural network models.  
Various techniques for reducing the number of instances have been extensively studied and documented in the literature [14; 30; 31; 32; 33]. In this study, we select the Edited Nearest Neighbor (ENN) algorithm as our choice for instance reduction. Despite its moderate reduction rate [14], ENN is known for its efficiency, making it a suitable option for our purposes. Efficiency plays a crucial role in our study due to the large-scale nature of the datasets we use in our experiments, which are characterized by a high number of attributes. Additionally, ENN is readily available in popular open-source frameworks, ensuring its accessibility for implementation. It is important to note that this work should be seen as proof of concept, aiming to demonstrate the practicality of the proposed framework. While we have opted for the ENN algorithm, it is worth mentioning that any instance reduction algorithm can be utilized in the proposed framework, providing flexibility in the choice of techniques based on the characteristics of the datasets.  
The ENN algorithm was developed by [PERSON] in 1972 [PERSON] (1972). Starting with \(S=T\), this technique eliminates every instance in \(S\) if its class is different from the majority of its k nearest neighbors' classes (k is often 3) [PERSON] (1972). ENN eliminates noisy instances as well as border instances that are close to instances from other classes [PERSON] (1972). Therefore, it usually increases classification accuracy while having a lower reduction rate because it keeps the internal instances. The Edited Nearest Neighbor algorithm is presented in Algorithm 1.  
```
Data:\(T=[Instance_{1},\ldots,Instance_{m}]\) Result: Reduced dataset \(\mathbb{S}\) begin
2\(S=T\) for\(Instance_{i}\in S\) find the k-nearest neighbors to \(Instance_{i}\) find the class with the majority of points among the k-nearest neighbors. (Break ties randomly) if the class of \(Instance_{i}\) is different from the majority class, then \(S=S-Instance_{i}\) return\(S\)
```  
**Algorithm 1**Edited Nearest Neighbor (T) [PERSON] (1972)