16 research outputs found
Scalable Global Mutual Information Based Feature Selection Framework for Large Scale Datasets
Distributed Ensemble Feature Selection Framework for High-Dimensional and High-Skewed Imbalanced Big Dataset
SDCOR: Scalable Density-based Clustering for Local Outlier Detection in Massive-Scale Datasets
This paper presents a batch-wise density-based clustering approach for local
outlier detection in massive-scale datasets. Unlike the well-known traditional
algorithms, which assume that all the data is memory-resident, our proposed
method is scalable and processes the input data chunk-by-chunk within the
confines of a limited memory buffer. A temporary clustering model is built at
the first phase; then, it is gradually updated by analyzing consecutive memory
loads of points. Subsequently, at the end of scalable clustering, the
approximate structure of the original clusters is obtained. Finally, by another
scan of the entire dataset and using a suitable criterion, an outlying score is
assigned to each object called SDCOR (Scalable Density-based Clustering
Outlierness Ratio). Evaluations on real-life and synthetic datasets demonstrate
that the proposed method has a low linear time complexity and is more effective
and efficient compared to best-known conventional density-based methods, which
need to load all data into the memory; and also, to some fast distance-based
methods, which can perform on data resident in the disk.Comment: Highlights are shortened each to about 85 character
Legal perspective on possible fairness measures – A legal discussion using the example of hiring decisions
With the increasing use of AI in algorithmic decision making (e.g. based on neural networks), the question arises how bias can be excluded or mitigated. There are some promising approaches, but many of them are based on a ”fair” ground truth, others are based on a subjective goal to be reached, which leads to the usual problem of how to define and compute ”fairness”. The different functioning of algorithmic decision making in contrast to human decision making leads to a shift from a process-oriented to a result-oriented discrimination assessment. We argue that with such a shift society needs to determine which kind of fairness is the right one to choose for which certain scenario. To understand the implications of such a determination we explain the different kinds of fairness concepts that might be applicable for the specific application of hiring decisions, analyze their pros and cons with regard to the respective fairness interpretation and evaluate them from a legal perspective (based on EU law)