Boosting-based Autoencoder

• Adapted boosting mechanisms on simple autoencoders for outlier detection purposes specialised in discarding outliers in each boosting iteration.

• Surpassed state-of-the-art and classic anomaly detection strategies achieving an average AUCPR score of 46.2%.

• Examined the relationship between the data distribution and the correct prediction rate of BAE with respect to the other approaches proposed in the literature.

• Performed exhaustive experiments on the performances of BAE and their superiority on all test-beds according to statistical significance methods.

• Collaborated in writing and formalising the methodology’s workflow by relying on mathematical notation of algebra and first order logic.

Bardh Prenkaj
Bardh Prenkaj
Computer Scientist, PhD

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