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A new approach for optimal offline time-series segmentation with error bound guarantee
(Elsevier, 2021)
This paper proposes a new optimal approach, called OSFS, based on feasible space (FS) Liu et al. (2008)[, that minimizes the number of segments of the approximation and guarantees the error bound using the L∞-norm. On the ...
SI(FS)2: Fast simultaneous instance and feature selection for datasets with many features
(Elsevier, 2021)
Data reduction is becoming increasingly relevant due to the enormous amounts of data that are constantly being produced in many fields of research. Instance selection is one of the most widely used methods for this task. ...
Area Maximizing Surfaces in Lorentzian Spaces
(Springer, 2021)
In this paper we provide new results for area maximizing compact spacelike surfaces with
boundary embedded in Lorentz-Minkowski space, as well as establish the uniqueness of the
Dirichlet problem for maximal graphs in ...
On the geometry of stationary Galilean spacetimes
(Springer, 2021)
In this work we introduce a new family of non-relativistic spacetimes: standard stationary Galilean spacetimes, which constitute the local geometric model of stationary Galilean spacetimes. We also study the geodesic ...
Effective Feature Selection Method for Class-Imbalance Datasets Applied to Chemical Toxicity Prediction
(American Chemical Society, 2021)
During the drug development process, it is common to carry out toxicity tests and adverse effect studies, which are essential to guarantee patient safety and the success of the research. The use of in silico quantitative ...
Cooperative coevolutionary instance selection for multilabel problems
(Elsevier, 2021)
Multilabel classification as a data mining task has recently attracted greater research interest. Many
current data mining applications address problems having instances that belong to more than one
class, which requires ...
GEML: A grammar-based evolutionary machine learning approach for design-pattern detection
(Elsevier, 2021)
Design patterns (DPs) are recognised as a good practice in software development. However, the lack of appropriate documentation often hampers traceability, and their benefits are blurred among thousands of lines of code. ...
Instance selection for multi-label learning based on a scalable evolutionary algorithm
(IEEE, 2021)
Multi-label classification has recently attracted
greater research interest as a data mining task. Many current ap-
plications in data mining address problems having instances that
belong to more than one class, which ...
Rule-based preprocessing for data stream mining using complex event processing
(Wiley, 2021)
Data preprocessing is known to be essential to produce accurate data from which mining methods are able to extract valuable knowledge. When data constantly arrives from one or more sources, preprocessing techniques need ...