KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift

Losing V, Hammer B, Wersing H (2016)
In: 2016 IEEE 16th International Conference on Data Mining (ICDM). Piscataway, NJ: IEEE: 291-300.

Konferenzbeitrag | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
Data Mining in non-stationary data streams is gaining more attention recently, especially in the context of Internet of Things and Big Data. It is a highly challenging task, since the fundamentally different types of possibly occurring drift undermine classical assumptions such as data independence or stationary distributions. Available algorithms are either struggling with certain forms of drift or require a priori knowledge in terms of a task specific setting. We propose the Self Adjusting Memory (SAM) model for the k Nearest Neighbor (kNN) algorithm since kNN constitutes a proven classifier within the streaming setting. SAM-kNN can deal with heterogeneous concept drift, i.e different drift types and rates, using biologically inspired memory models and their coordination. It can be easily applied in practice since an optimization of the meta parameters is not necessary. The basic idea is to construct dedicated models for the current and former concepts and apply them according to the demands of the given situation. An extensive evaluation on various benchmarks, consisting of artificial streams with known drift characteristics as well as real world datasets is conducted. Thereby, we explicitly add new benchmarks enabling a precise performance evaluation on multiple types of drift. The highly competitive results throughout all experiments underline the robustness of SAM-kNN as well as its capability to handle heterogeneous concept drift.
Stichworte
data mining; optimisation; pattern classification; Big Data; Internet of Things; KNN classifier; SAM-kNN robustness; data mining; k nearest neighbor algorithm; metaparameter optimization; nonstationary data streams; performance evaluation; self adjusting memory model; Adaptation models; Benchmark testing; Biological system modeling; Data mining; Heuristic algorithms; Prediction algorithms; Predictive models; Data streams; concept drift; data mining; kNN
Erscheinungsjahr
2016
Titel des Konferenzbandes
2016 IEEE 16th International Conference on Data Mining (ICDM)
Seite(n)
291-300
Konferenz
International Conference On Data Mining
Konferenzort
Barcelona
Konferenzdatum
2016-12-12 – 2016-12-15
ISBN
978-1-5090-5473-2
Page URI
https://pub.uni-bielefeld.de/record/2907622

Zitieren

Losing V, Hammer B, Wersing H. KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift. In: 2016 IEEE 16th International Conference on Data Mining (ICDM). Piscataway, NJ: IEEE; 2016: 291-300.
Losing, V., Hammer, B., & Wersing, H. (2016). KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift. 2016 IEEE 16th International Conference on Data Mining (ICDM), 291-300. Piscataway, NJ: IEEE. doi:10.1109/ICDM.2016.0040
Losing, V., Hammer, B., and Wersing, H. (2016). “KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift” in 2016 IEEE 16th International Conference on Data Mining (ICDM) (Piscataway, NJ: IEEE), 291-300.
Losing, V., Hammer, B., & Wersing, H., 2016. KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift. In 2016 IEEE 16th International Conference on Data Mining (ICDM). Piscataway, NJ: IEEE, pp. 291-300.
V. Losing, B. Hammer, and H. Wersing, “KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift”, 2016 IEEE 16th International Conference on Data Mining (ICDM), Piscataway, NJ: IEEE, 2016, pp.291-300.
Losing, V., Hammer, B., Wersing, H.: KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift. 2016 IEEE 16th International Conference on Data Mining (ICDM). p. 291-300. IEEE, Piscataway, NJ (2016).
Losing, Viktor, Hammer, Barbara, and Wersing, Heiko. “KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift”. 2016 IEEE 16th International Conference on Data Mining (ICDM). Piscataway, NJ: IEEE, 2016. 291-300.
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2019-09-06T09:18:42Z
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