Enhash: A Fast Streaming Algorithm For Concept Drift Detection
Aashi Jindal, Prashant Gupta, Debarka Sengupta, Jayadeva Jayadeva · ESANN 2021 proceedings · 2021
We propose Enhash, a fast ensemble learner that detects concept drift in a data stream.A stream may consist of abrupt, gradual, virtual, or recurring events, or a mixture of various types of drift.Enhash employs projection hash to insert an incoming sample.Benchmark tests on 6 artificial and 4 real data sets consisting of various types of drift show that Enhash is competitive with stateof-the-art ensemble learners while being significantly faster.It also has moderate resource requirements. 59