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RDDM: Reactive drift detection method

2017; Elsevier BV; Volume: 90; Linguagem: Inglês

10.1016/j.eswa.2017.08.023

1873-6793

Roberto Souto Maior de Barros, Danilo R.L. Cabral, Paulo Gonçalvés, Silas Garrido Teixeira de Carvalho Santos,

Network Security and Intrusion Detection

Concept drift detectors are online learning software that mostly attempt to estimate the drift positions in data streams in order to modify the base classifier after these changes and improve accuracy. This is very important in applications such as the detection of anomalies in TCP/IP traffic and/or frauds in financial transactions. Drift Detection Method (DDM) is a simple, efficient, well-known method whose performance is often impaired when the concepts are very long. This article proposes the Reactive Drift Detection Method (RDDM), which is based on DDM and, among other modifications, discards older instances of very long concepts aiming to detect drifts earlier, improving the final accuracy. Experiments run in MOA, using abrupt and gradual concept drift versions of different dataset generators and sizes (48 artificial datasets in total), as well as three real-world datasets, suggest RDDM beats the accuracy results of DDM, ECDD, and STEPD in most scenarios.