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Communication Dans Un Congrès Année : 2015

Classification with a reject option under Concept Drift: the Droplets Algorithm

Résumé

In this paper a new on-line algorithm is proposed (the Droplets algorithm) for dealing with concept drifts and to produce reliable predictions. The two main characteristics of this algorithm are that it is able to adapt to different types of drifts without making any assumptions regarding their type or when they occur, and can provide reliable predictions in a non-stationary environment without using a fixed confidence threshold. Experimental results on five datasets based on Random RBF and Rotating Hyperplane generators as well as a new semi-synthetic dataset based weather temperatures show that, by discarding difficult observations, the Droplets algorithm manages to obtain the best average accuracy against ten classifiers. The results also indicate that the algorithm manages to provide reliable prediction by accurately distinguishing which observations are easily classifiable.
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Dates et versions

hal-01213494 , version 1 (08-10-2015)

Identifiants

  • HAL Id : hal-01213494 , version 1

Citer

Pierre-Xavier Loeffel, Christophe Marsala, Marcin Detyniecki. Classification with a reject option under Concept Drift: the Droplets Algorithm. The IEEE International Conference on Data Science and Advanced Analytics (DSAA'2015), Oct 2015, Paris, France. ⟨hal-01213494⟩
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