## 2 Methods
### KNN-Is and SMOTE in Big Data
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* _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON] [PERSON] Data analysis with arbitrary error measures approximated by piece-wise quadratic PQSQ functions. In Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 8-13 July 2018. [CrossRef]
* _[PERSON] (2011)_ [PERSON]; [PERSON] _The Dissimilarity Representation for Non-Euclidean Pattern Recognition, a Tutorial_; Technical Report; Delf University of Technology: Delft, The Netherlands, 2011. * _[PERSON] (2012)_ [PERSON]; [PERSON] The dissimilarity space: Bridging structural and statistical pattern recognition. _Pattern Recognit. Lett._**2012**, 33, 826-832. [CrossRef]
* _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Dissimilarity-Based Linear Models for Corporate Bankruptcy Prediction. _Comput. Econ._**2019**, 53, 1019-1031. [CrossRef]
* _[PERSON] et al. (2023)_ [PERSON]; [PERSON]; [PERSON]. Indian Pines Dataset. Available online: [[https://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes#Indian_Pines](https://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes#Indian_Pines)]([https://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes#Indian_Pines](https://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes#Indian_Pines)) (accessed on 2 December 2023). * Infrared Imaging Spectrometer. Available online: [[https://aviris.jpl.nasa.gov/data/index.html](https://aviris.jpl.nasa.gov/data/index.html)]([https://aviris.jpl.nasa.gov/data/index.html](https://aviris.jpl.nasa.gov/data/index.html)) (accessed on 2 December 2023). * _[PERSON] et al. (2020)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem. _Appl. Sci._**2020**, _10_, 1276. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON] Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions. _Pattern Anal. Mach.