Minor improvement in flow

paper.md

@@ -44,7 +44,7 @@ A substantial body of literature in the field of machine learning and statistics
 The literature encompasses a myriad of oversampling and undersampling techniques to approach the class imbalance issue. These include SMOTE [@Chawla:2002] which operates by generating synthetic examples along the lines joining existing points, SMOTE-N and SMOTE-NC [@Chawla:2002] which are variants of SMOTE that can deal with categorical data. The sheer number of SMOTE variants makes them a body of literature on their own. Notably, the most widely cited variant of SMOTE is BorderlineSMOTE [@Han:2005]. Other well-established oversampling techniques include RWO [@Zhang:2014] and ROSE [@Menardi:2012] which operate by estimating probability densities and sampling from them to generate synthetic points. On the other hand, the literature also encompasses many undersampling techniques. Cluster undersampling [@Lin:2016] and condensed nearest neighbors [@Hart:1968] are two prominent examples which attempt to reduce the number of points while preserving the structure or classification of the data. Furthermore, methods that combine oversampling and undersampling such as SMOTETomek [@Zeng:2016] are also present. The motivation behind these methods is that when undersampling is not random, it can filter out noisy or irrelevant oversampled data. Lastly, resampling with ensemble learning has also been presented in the literature with EasyEnsemble being them most well-known approach of that type [@Liu:2009].
 
 
-The existence of a toolbox with techniques that harness this wealth of research is imperative to the development of novel approaches to the class imbalance problem and for machine learning research broadly. Aside from addressing class imbalance in a general machine learning research setting, the toolbox can help in class imbalance research settings by making it possible to juxtapose different methods, compose them together, or form variants of them without having to reimplement them from scratch. In prevalent programming languages, such as Python, a variety of such toolboxes already exist, such as imbalanced-learn [@Lematre:2016] and SMOTE-variants [@Kovács:2019]. Meanwhile, Julia [@julia], a well known programming language with over 40M downloads [@DataCamp:2023], has been lacking a similar toolbox to address the class imbalance issue in general multi-class and heterogeneous data settings. This has served as the primary motivation for the creation of the `Imbalance.jl` toolbox.
+The existence of a toolbox with techniques that harness this wealth of research is imperative to the development of novel approaches to the class imbalance problem and for machine learning research broadly. Aside from addressing class imbalance in a general machine learning research setting, such a toolbox can help in class imbalance research settings by making it possible to juxtapose different methods, compose them together, or form variants of them without having to reimplement them from scratch. In prevalent programming languages, such as Python, a variety of such toolboxes already exist, such as imbalanced-learn [@Lematre:2016] and SMOTE-variants [@Kovács:2019]. Meanwhile, Julia [@julia], a well known programming language with over 40M downloads [@DataCamp:2023], has been lacking a similar toolbox to address the class imbalance issue in general multi-class and heterogeneous data settings. This has served as the primary motivation for the creation of the `Imbalance.jl` toolbox, which we introduce in the subsequent section.
 
 # Imbalance.jl