The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant attributes, allow making models of classification simpler and easy to understand. Depending on the method to apply: starting point, search organization, evaluation strategy, and the stopping criterion, there is an added cost to the classification algorithm that we are going...

Coalescing is a unary operator applicable to temporal databases; it is similar to duplicate elimination in conventional databases. Tuples in a temporal relation that agree on the explicit attribute values and that have adjacent or overlapping time periods are candidates for coalescing. Uncoalesced relations can arise in many ways, e.g., via a projection or union operator, or by not enforcing co...

The class 4 P-type ATPases ("flippases") maintain membrane asymmetry by translocating phosphatidylethanolamine and phosphatidylserine from the outer leaflet to the cytosolic leaflet of the plasma membrane. In Saccharomyces cerevisiae, five related gene products (Dnf1, Dnf2, Dnf3, Drs2, and Neo1) are implicated in flipping of phosphatidylethanolamine, phosphatidylserine, and phosphatidylcholine....

Attribute reduction is an important inductive learning issue addressed by the Rough Sets society. Most existing works on this issue use the minimal attribute bias, i.e., searching for reducts with the minimal number of attributes. But this bias does not work well for datasets where different attributes have different sizes of domains. In this paper, we propose a more reasonable strategy called ...

State-of-the-art session variability compensation for speaker recognition are generally based on various linear statistical models of the Gaussian Mixture Model (GMM) mean super-vectors, while frontend features are only processed by standard normalization techniques. In this study, we propose a front-end channel compensation frame-work using mixture-localized linear transforms that operate befo...

The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant attributes, allow making models of classification simpler and easy to understand. Depending on the method to apply: starting point, search organization, evaluation strategy, and the stopping criterion, there is an added cost to the classification algorithm that we are going...