Outlier Selection and One-Class Classification

You can read my PhD thesis online or download it as PDF (~10MB).

What is common in a terrorist attack, a forged painting, and a rotten apple? The answer is: all three are anomalies; they are real-world observations that deviate from what is considered to be normal. Detecting anomalies is of utmost importance because an undetected anomaly can be dangerous or expensive. A human domain expert may suffer from three cognitive limitations: fatigue, information overload, and emotional bias. The cognitive limitations will hamper the detection of anomalies. Outlier-selection and one-class classification algorithms are capable of automatically classifying data points as outliers in large amounts of data. In this thesis we study to what extent outlier-selection and one-class classification algorithms can support domain experts with real-world anomaly detection.

Figures

The figures in the thesis are created using Python, MATLAB and TikZ. The TikZ code of the figures can be found in /figures/tikz. To compile all the figures to PDF, I wrote a script called tikz2pdf.

$ tikz2pdf figures/tikz/*.tikz --template figures/thesis-template.tex --output figures/pdf/

Below are some figures from the thesis. Please note that these are rendered with a different font. Also, the conversion from PDF to PNG with ImageMagick isn't all that great.

figures/tikz/bg-banana-roc.tikz

figures/tikz/bg-multiclass.tikz

figures/tikz/eval-boxplot-preprocessing-pca.tikz

figures/tikz/mlc-mapping-auc-overview.tikz

figures/tikz/sos-densities.tikz

figures/tikz/sos-graph-matlab-binding.tikz

figures/tikz/sos-graphs-sample.tikz

figures/tikz/sos-nemenyi.tikz