One vs Previous and Similar Classes Learning -- A Comparative Study
This work addresses the problem of efficiently updating multi-class classification models for practitioners dealing with evolving datasets, offering an incremental improvement over existing methods.
This paper proposes three new learning paradigms for multi-class classification that allow models to be updated without retraining from scratch. The proposed methods are faster at updating than the baseline, with two also being faster at initial training on larger datasets, while maintaining comparable classification performance.
When dealing with multi-class classification problems, it is common practice to build a model consisting of a series of binary classifiers using a learning paradigm which dictates how the classifiers are built and combined to discriminate between the individual classes. As new data enters the system and the model needs updating, these models would often need to be retrained from scratch. This work proposes three learning paradigms which allow trained models to be updated without the need of retraining from scratch. A comparative analysis is performed to evaluate them against a baseline. Results show that the proposed paradigms are faster than the baseline at updating, with two of them being faster at training from scratch as well, especially on larger datasets, while retaining a comparable classification performance.