The phrase "attributes construction" is not commonly used in written English and may be unclear without context.
It could be used in discussions related to programming, data modeling, or design, where attributes are defined or constructed.
Example: "In our database schema, the attributes construction is crucial for ensuring data integrity and proper relationships between tables."
Alternatives: "attribute development" or "attribute definition".
The AAT contains over 120,000 terms that describe objects, textural materials, images, architecture and material culture from antiquity to the present (focused mainly on the Western World) and provides vocabulary to describe the materials and techniques related to physical attributes construction, and conservation.
As an example, comparing the vulnerability attributes of the highlighted building in Fig. 8 with those of the left adjacent building, one concludes that the position in block contributed the most to collapse of the highlighted building and non-collapse of the left building, since the other attributes (construction epoch, typology, soil, slope, and so on) are very similar.
The general process of defining a new attribute as a function of two or more other attributes is referred to as constructive induction, or attribute construction, and was first described by Michalski (1983).
Defining a new attribute as a function of two or more other attributes is referred to as constructive induction, or attribute construction, and was first described by Michalski et al[ 24].
It is significant that incorporation of the expertise of human experts by new attribute construction contributes to the effective learning of the data classifier.
Thus in trade-off the attribute construction costs tends to be accentuated.
Criteria include one cost attribute: construction costs, and three benefit attributes: travel time, road safety and air quality.
"Compare the swing for worst to best of the attribute construction costs with the swing from worst to best of the attribute travel time benefits.
At the heart of MDR is an attribute construction algorithm, pooling levels from multiple discrete factors to create a new discrete attribute[ 23].
For each locus or multi-locus combination, attribute construction is performed to make a single variable with two categories: high risk and low-risk.
While datasets with a two-way interaction and no main effects are useful, several simulations with this attribute construction already exist [ 15, 32].
