Version 1
: Received: 26 December 2021 / Approved: 27 December 2021 / Online: 27 December 2021 (12:12:54 CET)
How to cite:
So, B.; Valdez, E.A. The SAMME.C2 Algorithm for Severely Imbalanced Multi-class Classification. Preprints2021, 2021120427. https://doi.org/10.20944/preprints202112.0427.v1
So, B.; Valdez, E.A. The SAMME.C2 Algorithm for Severely Imbalanced Multi-class Classification. Preprints 2021, 2021120427. https://doi.org/10.20944/preprints202112.0427.v1
So, B.; Valdez, E.A. The SAMME.C2 Algorithm for Severely Imbalanced Multi-class Classification. Preprints2021, 2021120427. https://doi.org/10.20944/preprints202112.0427.v1
APA Style
So, B., & Valdez, E.A. (2021). The SAMME.C2 Algorithm for Severely Imbalanced Multi-class Classification. Preprints. https://doi.org/10.20944/preprints202112.0427.v1
Chicago/Turabian Style
So, B. and Emiliano A. Valdez. 2021 "The SAMME.C2 Algorithm for Severely Imbalanced Multi-class Classification" Preprints. https://doi.org/10.20944/preprints202112.0427.v1
Abstract
Classification predictive modeling involves the accurate assignment of observations in a dataset to target classes or categories. There is an increasing growth of real-world classification problems with severely imbalanced class distributions. In this case, minority classes have much fewer observations to learn from than those from majority classes. Despite this sparsity, a minority class is often considered the more interesting class yet developing a scientific learning algorithm suitable for the observations presents countless challenges. In this article, we suggest a novel multi-class classification algorithm specialized to handle severely imbalanced classes based on the method we refer to as SAMME.C2. It blends the flexible mechanics of the boosting techniques from SAMME algorithm, a multi-class classifier, and Ada.C2 algorithm, a cost-sensitive binary classifier designed to address highly class imbalances. Not only do we provide the resulting algorithm but we also establish scientific and statistical formulation of our proposed SAMME.C2 algorithm. Through numerical experiments examining various degrees of classifier difficulty, we demonstrate consistent superior performance of our proposed model.
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
