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Found 7 papers, 2 papers with code
Impact of Biases in Big Data
In this work, we provide a review of different sorts of biases in (big) data sets in machine learning.
On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage
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.
Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations
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.
The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study
It reveals new and up-to-date insights into what the 10 most prolific topics in machine learning research are.
Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?
We apply it to a commercial data set from Brazil that consists of 3. 6M customers and 820K inspection results.
Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets
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%.
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
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.
