no code implementations • 2 Mar 2018 • Patrick Glauner, Petko Valtchev, Radu State
In this work, we provide a review of different sorts of biases in (big) data sets in machine learning.
no code implementations • 17 Jan 2018 • Patrick Glauner, Radu State, Petko Valtchev, Diogo Duarte
Our models have the potential to generate significant economic value in a real world application, as they are being deployed in a commercial software for the detection of irregular power usage.
no code implementations • 9 Sep 2017 • Patrick Glauner, Niklas Dahringer, Oleksandr Puhachov, Jorge Augusto Meira, Petko Valtchev, Radu State, Diogo Duarte
Second, in order to allow human experts to feed their knowledge in the decision loop, we propose a method for visualizing prediction results at various granularity levels in a spatial hologram.
1 code implementation • 29 Mar 2017 • Patrick Glauner, Manxing Du, Victor Paraschiv, Andrey Boytsov, Isabel Lopez Andrade, Jorge Meira, Petko Valtchev, Radu State
It reveals new and up-to-date insights into what the 10 most prolific topics in machine learning research are.
1 code implementation • 13 Feb 2017 • Patrick Glauner, Angelo Migliosi, Jorge Meira, Petko Valtchev, Radu State, Franck Bettinger
We apply it to a commercial data set from Brazil that consists of 3. 6M customers and 820K inspection results.
no code implementations • 4 Jul 2016 • Patrick Glauner, Jorge Meira, Lautaro Dolberg, Radu State, Franck Bettinger, Yves Rangoni, Diogo Duarte
Using the neighborhood features instead of only analyzing the time series has resulted in appreciable results for Big Data sets for varying NTL proportions of 1%-90%.
no code implementations • 2 Jun 2016 • Patrick Glauner, Jorge Augusto Meira, Petko Valtchev, Radu State, Franck Bettinger
Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science.