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Section 3 introduces the proposed methodology and aspects of feature sets evaluation.
Cross-validation involves repeatedly splitting the samples in the dataset into training and test sets, performing all aspects of feature selection and model building on the training set, and evaluating the model's performance on the test set.
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As defined by Donglas Laney [33], big data problems mainly have three aspects of features of data growth, i.e. having increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources).
This is not surprising since a given episodic event triggers co-activation of a unique cell assembly through which various groups of responsive cells simultaneously come together to encode different aspects of features of the event (from general to specific features) [10] [12], [26].
We introduce a concept of network connectivity to quantify the aspect of feature reduction.
The aspect of feature detection and validation is investigated to reliably detect the predominant features in the environment.
An important aspect of feature selection methods is the stability of obtained ordered lists [ 1, 12].
In addition to F1 measure, dimension reduction rate is another important aspect of feature selection.
The key principle is that in computing cross-validation error rates, within each cross-validation fold only training observations can be used in any aspect of feature selection or model development.
The hard logic implements exactly the specification of a feature, while the soft logic deals with the adaptation aspects of a feature, i.e. resolving interaction problems and making features work together.
One important aspect of feature-based accounts of conceptual representation is that shared information forms the basis of category organization.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com