67,160 research outputs found
Efficient Database Generation for Data-driven Security Assessment of Power Systems
Power system security assessment methods require large datasets of operating
points to train or test their performance. As historical data often contain
limited number of abnormal situations, simulation data are necessary to
accurately determine the security boundary. Generating such a database is an
extremely demanding task, which becomes intractable even for small system
sizes. This paper proposes a modular and highly scalable algorithm for
computationally efficient database generation. Using convex relaxation
techniques and complex network theory, we discard large infeasible regions and
drastically reduce the search space. We explore the remaining space by a highly
parallelizable algorithm and substantially decrease computation time. Our
method accommodates numerous definitions of power system security. Here we
focus on the combination of N-k security and small-signal stability.
Demonstrating our algorithm on IEEE 14-bus and NESTA 162-bus systems, we show
how it outperforms existing approaches requiring less than 10% of the time
other methods require.Comment: Database publicly available at:
https://github.com/johnnyDEDK/OPs_Nesta162Bus - Paper accepted for
publication at IEEE Transactions on Power System
Types of cost in inductive concept learning
Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In real-world applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted as a type of cost measure). A few papers have investigated the cost of misclassification errors. Very few papers have examined the many other types of cost. In this paper, we attempt to create a taxonomy of the different types of cost that are involved in inductive concept learning. This taxonomy may help to organize the literature on cost-sensitive learning. We hope that it will inspire researchers to investigate all types of cost in inductive concept learning in more depth
Autoencoders for strategic decision support
In the majority of executive domains, a notion of normality is involved in
most strategic decisions. However, few data-driven tools that support strategic
decision-making are available. We introduce and extend the use of autoencoders
to provide strategically relevant granular feedback. A first experiment
indicates that experts are inconsistent in their decision making, highlighting
the need for strategic decision support. Furthermore, using two large
industry-provided human resources datasets, the proposed solution is evaluated
in terms of ranking accuracy, synergy with human experts, and dimension-level
feedback. This three-point scheme is validated using (a) synthetic data, (b)
the perspective of data quality, (c) blind expert validation, and (d)
transparent expert evaluation. Our study confirms several principal weaknesses
of human decision-making and stresses the importance of synergy between a model
and humans. Moreover, unsupervised learning and in particular the autoencoder
are shown to be valuable tools for strategic decision-making
Temporary staffing services: a data mining perspective
Research on the temporary staffing industry discusses different topics ranging from workplace safety to the internationalization of temporary labor. However, there is a lack of data mining studies concerning this topic. This paper meets this void and uses a financial dataset as input for the estimated models. Bagged decision trees were utilized to cope with the high dimensionality. Two bagged decision trees were estimated: one using the whole dataset and one using the top 12 predictors. Both had the same predictive performance. This means we can highly reduce the computational complexity, without losing accuracy
Temporary staffing services: a data mining perspective
Research on the temporary staffing industry discusses different topics ranging from workplace safety to the internationalization of temporary labor. However, there is a lack of data mining studies concerning this topic. This paper meets this void and uses a financial dataset as input for the estimated models. Bagged decision trees were utilized to cope with the high dimensionality. Two bagged decision trees were estimated: one using the whole dataset and one using the top 12 predictors. Both had the same predictive performance. This means we can highly reduce the computational complexity, without losing accuracy
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