  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.
