## 2 Related Work  
SDN and knowledge-based networking (KDN) paradigms are instrumental in implementing frequent network reconfiguration and reoptimization within a DWDM network [9; 10]. Central to SDN and KDN is an accurate estimation of transmission quality [11; 12]. Therefore, a number of ML-based models have been proposed that synthetically create the database and apply ML to such data [13; 14]. However, modeling methods used to create synthetic databases often place too high computational requirements for real-time prediction, making them unscalable to large networks. Moreover, these models rely on a number of parameters that may not be available to a network operator. In [15; 16], the authors claim that ML technology will become an important means to providing indispensable technical support to further increase communication capacity and improve future communication stability by effectively identifying physical impairments in the network [17]. In the new generation passive optical networks (PONs) [18], ML-based algorithms have also been successfully applied, particularly to the load-balancing problem [19]. Here, we follow an alternative approach to the application of ML to the automatic reconfiguration of an optical network that relies only on the data that are available to a network operator via the control plane. Such an approach is distinct from the one followed in [10; 13]. Similar lightpath QoT estimation problems were also addressed in [20; 21], whereby the solution presented there mainly focused on an application of the SVM classifier; meanwhile, in [22], the authors used the random forest algorithm. A similar approach was also used in [23]. In all these cases, the system was trained and tested on artificial data, which differs qualitatively from the approach taken in this paper. As already mentioned here, we rely only on the data that are available to a network operator via the control plane. The resulting training dataset is intrinsically imbalanced and, as already mentioned, potentially small, thus making the adoption of ML methods that are specially tailored for handling imbalanced datasets necessary. In [24], the author compared different means to dealing with class imbalance, including data resampling and one-class classification. It was concluded that binary classification with imbalance compensation is superior to single-class classification. A similar problem to the one presented here was discussed in [25]. The results presented in this work suggest that binary classification may be more effective than single-class classification for data with complex multimodal distributions. In contrast, [PERSON] and [PERSON] used single-class classification for unbalanced data and showed that it could outperform binary classification if the unbalanced factor is high [26]. Their work inspired further research into the utility of binary and one-class algorithms for optical network path classification, such as in [7, 8]. The results suggested that the one-class approach can be a viable alternative to standard binary classification in this application domain. However, binary classification with class imbalance compensated by class weights still works reasonably well, and the more refined SMOTE [27] and ROSE [28] techniques do not lead to improved predictive performance. This work, therefore, revisits the binary classification approach using class weights for imbalance compensation. We extensively and systematically explore the potential of improving the prediction quality by evaluating the utility of different attribute subsets corresponding to different physical network path properties, using a broader scope of classification algorithms, and tuning their hype-parameters. The literature are includes interesting attempts use artificial neural networks (ANNs), where the inputs are channel loadings and launch power settings [29] or source-destination nodes, link occupation, modulation format, path length, and data rate [30].