Multi-imbalance

About the package

multi-imbalance is an open-source Python library for multi-class imbalanced classification, which equips constantly growing Python community of machine learning researchers and practitioners with proper tools to deal with multi-class imbalance problems. The package follows the code conventions of popular sklearn package and provides efficient implementations of state-of-the-art decomposition techniques as well as both novel and classic resampling approaches for multi-class imbalanced classification. The code of the library is rigorously covered by unit tests, documented with sphinx library, and meets PEP8 standard. The code is available on GitHub

The package contains the implementation of the following methods:

• One-vs-One (OVO) and One-vs-all (OVA) ensembles [2],
• Error-Correcting Output Codes (ECOC) [1] with dense, sparse and complete encoding [9] ,
• Global-CS [4],
• Static-SMOTE [10],
• Mahalanobis Distance Oversampling [3],
• Similarity-based Oversampling and Undersampling Preprocessing (SOUP) [5],
• SPIDER3 cost-sensitive pre-processing [8].
• Multi-class Roughly Balanced Bagging (MRBB) [7],
• SOUP Bagging [6],

Learn more from the video:

Examples

Below we present an exemplary usage of SOUP to resample the training data. We present the result of this preprocessing. The dataset we have used is Glass.

Below we present usage of SOUP along with OVO ensemble. Standard decision tree classifier from sklearn was used as component binary classifier.

As one can see, the usage of our library is very intuitive.

For more examples please refer to the exemplary notebook and docs

References

• [1] Dietterich, T., and Bakiri, G. Solving multi-class learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2 (02 1995), 263–286.
• [2] Fernández, A., López, V., Galar, M., del Jesus, M., and Herrera, F. Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches. Knowledge-Based Systems 42 (2013), 97 – 110.
• [3] Abdi, L., and Hashemi, S. To combat multi-class imbalanced problems by means of over-sampling techniques. IEEE Transactions on Knowledge and Data Engineering 28 (January 2016), 238–251.
• [4] Zhou, Z., and Liu, X. On multi-class cost-sensitive learning. In Proceedings of the 21st National Conference on Artificial Intelligence - Volume 1 (2006), AAAI’06, AAAI Press, pp. 567–572.
• [5] Janicka, M., Lango, M., and Stefanowski, J. Using information on class interrelations to improve classification of multi-class imbalanced data: A new resampling algorithm. International Journal of Applied Mathematics and Computer Science 29 (December 2019).
• [6] Lango, M., and Stefanowski, J. SOUP-Bagging: a new approach for multi-class imbalanced data classification. PP-RAI ’19: Polskie Porozumienie na Rzecz Sztucznej Inteligencji (2019).
• [7] Lango, M., and Stefanowski, J. Multi-class and feature selection extensions of roughly balanced bagging for imbalanced data. J Intell Inf Syst 50 (2017), 97–127
• [8] Wojciechowski, S., Wilk, S., and Stefanowski, J. An algorithm for selective preprocessing of multi-class imbalanced data. In Proceedings of the 10th International Conference on Computer Recognition Systems (05 2017), pp. 238–247.
• [9] Kuncheva, L. Combining Pattern Classifiers: Methods and Algorithms. Wiley (2004).
• [10] Fernández-Navarro, F., Hervás-Martínez, C., and Antonio Gutiérrez, P. A dynamic over-sampling procedure based on sensitivity for multi-class problems. Pattern Recognition, 44(8), 1821–1833 (2011).