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Adaptive Decision Forest: An incremental machine learning framework
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DOI：10.1016/j.patcog.2021.108345.png)
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Abstract
En 中文
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 nine publicly available natural datasets and one synthetic dataset, and compare the performance of ADF against the performance of eight state-of-the-art techniques. We also examine the effectiveness of ADF in some challenging situations. Our experimen-tal results, including statistical sign test and Nemenyi test analyses, indicate a clear superiority of the proposed framework over the state-of-the-art techniques. (c) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Incremental learning
Decision forest algorithm
Concept drift
Big data
Online learning
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