Standard oversampling methods can be used to improve the dataset class distribution; however, they do not consider the ordinal relationship between the classes. The proposed CWOS-Ord method aims to address this
Then, to solve the problems of traditional cluster-based oversampling, we propose a k-means cluster-based filtering strategy. Define a matrix of original sample classes, perform class difference calculations on the clustered samples, and screen out ‘‘safe samples” that have no change in sample...
Hence, an under-sampling ap- proach is to decrease the skewed distribution of MA and MI by lowering the size of MA. Generally, the performances of over-sam- pling approaches are worse than that of under-sampling ap- proaches (Drummond & Holte, 2003). One simple method of under-sampling...
At the time of writing the library does not support respecting the cluster vertex limit, so after processing this value might be increased over what was selected in the ui. And if the upper limit of 256 is exceeded, the application will fail to load the scene for now....
the proposed solution computes exact BC values of nodes only with respect to a subset of nodes of a graph, named target set. When the target set includes all the nodes of a given graph, the solution converges towards Brandes’ algorithm, but with the additional overhead due to the creation...
in a task with lots of images containing grass or plants there will be an abundance of different shades of green and thus the network will learn very quickly to reproduce similar colors over the test set. On the contrary, an image that is very different from the majority of images in the...
the sensors are stationery and are all assigned to a fixed cluster at the initiation of the network that remains unchanged over the entire lifetime of the sensor network. After the clusters are formed at the network start-up, the consecutive data cycles involve random sampling (or sensing) ...
Many multi-objective optimization problems in the real world have conflicting objectives, and these objectives change over time, known as dynamic multi-objective optimization problems (DMOPs). In recent years, transfer learning has attracted growing attention to solve DMOPs, since it is capable of ...
The Centernet network, as an excellent member of the anchor-free model, has the feature of a large output resolution with only four downsampling rates and a good detection effect for small targets. Similarly, the confidence lower limit value is set to 0.6 to obtain the counting frame after ...
which corresponds to the sum of the potential energy and kinetic energy (total energy) of the quantum system; Ψ: wave function, which represents the state of particles in space; i: imaginary unit; ℏ: reduced Planck's constant; ∂Ψ∂t: the variation of the wave function over time)...