Syed Yousaf Shah, Zengwen Yuan, et al.
Big Data 2017
Data streams are continuous flows of data points. Novel class detection is an important part of data stream mining. A novel class is a newly emerged class that has not previously been modeled by the classifier over the input stream. This paper proposes deep embedding for novel class detection - a novel approach that combines feature learning using denoising autoencoding with novel class detection. A denoising autoencoder is a neural network with hidden layers aiming to reconstruct the input vector from a corrupted version. A nonparametric multidimensional change point detection approach is also proposed, to detect concept-drift (the change of data feature values over time). Experiments on several real datasets show that the approach significantly improves the performance of novel class detection.
Syed Yousaf Shah, Zengwen Yuan, et al.
Big Data 2017
Kubilay Atasu, Thomas Parnell, et al.
Big Data 2017
Antara Palit, Mudhakar Srivatsa, et al.
Big Data 2017
Karthikeyan Natesan Ramamurthy, Dennis Wei, et al.
Big Data 2017