Adaptive Decision Forest: An Incremental Machine Learning Framework
This work addresses incremental machine learning for classification, offering a solution for scenarios with evolving data and new classes, though it appears incremental in nature.
The paper tackles the problem of incremental learning for classification by proposing the Adaptive Decision Forest (ADF) framework, which uses a novel splitting strategy (iSAT) to handle new classes and concept drift without forgetting, and results show clear superiority over eight state-of-the-art techniques on six datasets.
In this study, we present an incremental machine learning framework called Adaptive Decision Forest (ADF), which produces a decision forest to classify new records. Based on our two novel theorems, we introduce a new splitting strategy called iSAT, which allows ADF to classify new records even if they are associated with previously unseen classes. ADF is capable of identifying and handling concept drift; it, however, does not forget previously gained knowledge. Moreover, ADF is capable of handling big data if the data can be divided into batches. We evaluate ADF on five publicly available natural data sets and one synthetic data set, and compare the performance of ADF against the performance of eight state-of-the-art techniques. Our experimental results, including statistical sign test and Nemenyi test analyses, indicate a clear superiority of the proposed framework over the state-of-the-art techniques.