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Discover LudwigThe phrase "attribute selection" is correct and usable in written English.
It is typically used in contexts related to data analysis, machine learning, or statistics, referring to the process of selecting relevant features or variables for a model.
Example: "In order to improve the accuracy of our predictive model, we need to focus on effective attribute selection."
Alternatives: "feature selection" or "variable selection".
Exact(60)
Separate models were developed on fingerprints selected using attribute selection modules rm-useless and CfsSubsetEval of Weka.
The agent-based attribute selection algorithm has been selected with 19 important features in Table 1.
Our framework selected data preprocessing, attribute selection technique, and learning algorithms automatically according to characteristics of the specific data set.
In feature selection, "cross-validation" was selected as the attribute selection mode, and tenfold splits as default were performed.
To select the most useful features, attribute selection algorithms, such as information gain and entropy, were employed.
Best attributes for the models were selected based on a supervised attribute selection method called wrapper subset evaluator (WSE).
And then, all heterogeneous costs with attribution information together are incorporated into the process of split attribute selection, called as HCAI-based split attribute selection.
Algorithms for attribute selection fall into two broad categories: filters and wrappers.
In the present work, a combination of attribute selection and data sampling is used to overcome these problems.
The critical parameters identification results to 8 (eight) significant constraints, which calculated using distance-based RELIEF attribute selection.
Many approaches are proposed to improve Naive Bayes, among which the attribute selection approach has demonstrated remarkable performance.
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