Density-Based Outlier Detection Benchmark on Synthetic Data (Thesis): Unterschied zwischen den Versionen

Aus IPD-Institutsseminar
Zur Navigation springen Zur Suche springen
(Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Lena Witterauf |email=uxegn@student.kit.edu |vortragstyp=Bachelorarbeit |betreuer=Georg Steinbuss |termin=Institutsseminar/2019-06-21 |…“)
 
 
(Eine dazwischenliegende Version von einem anderen Benutzer wird nicht angezeigt)
Zeile 4: Zeile 4:
 
|vortragstyp=Bachelorarbeit
 
|vortragstyp=Bachelorarbeit
 
|betreuer=Georg Steinbuss
 
|betreuer=Georg Steinbuss
|termin=Institutsseminar/2019-06-21
+
|termin=Institutsseminar/2019-06-21 Zusatztermin
|kurzfassung=tbd
+
|kurzfassung=Outlier detection is a popular topic in research, with a number of different approaches developed. Evaluating the effectiveness of these approaches however is a rather rarely touched field. The lack of commonly accepted benchmark data most likely is one of the obstacles for running a fair comparison of unsupervised outlier detection algorithms. This thesis compares the effectiveness of twelve density-based outlier detection algorithms in nearly 800.000 experiments over a broad range of algorithm parameters using the probability density as ground truth.
 
}}
 
}}

Aktuelle Version vom 17. Juni 2019, 11:55 Uhr

Vortragende(r) Lena Witterauf
Vortragstyp Bachelorarbeit
Betreuer(in) Georg Steinbuss
Termin Fr 21. Juni 2019
Vortragsmodus
Kurzfassung Outlier detection is a popular topic in research, with a number of different approaches developed. Evaluating the effectiveness of these approaches however is a rather rarely touched field. The lack of commonly accepted benchmark data most likely is one of the obstacles for running a fair comparison of unsupervised outlier detection algorithms. This thesis compares the effectiveness of twelve density-based outlier detection algorithms in nearly 800.000 experiments over a broad range of algorithm parameters using the probability density as ground truth.