Sentence examples for usefulness of feature from inspiring English sources

Wrapper methods measure the usefulness of feature subsets according to their predictive power, optimizing the subsequent induction procedure that uses the respective subset for classification [51, 60, 61, 62, 63].

These results underscore the usefulness of feature selection that, at least in the case studies presented in this paper, has helped to reduce the number of variables, and hence the required computational resources and time needed to create the training and test sets, without decreasing the overall performance.

Features are then removed one at a time until the target of 1500 is reached It is important to appreciate the difference between the feature selection performed during classifier construction and the feature ranking which is later used to assess the overall usefulness of features for predicting targets of a TF.

Gartner analyst Brian Prentice recently argued that simplicity is misunderstood and needs to be defined by the relevance and usefulness of features rather than the absence of features.

We asked about room creation process (e.g., availability of funding and who was involved), room characteristics (including the original purpose of the space and usefulness of features), support/training provided, and any additional impact related to SCALE-UP.

The former utilizes the targeted learning algorithm as a black box to evaluate the usefulness of features according to their predictive performance [ 36], while the latter selects features according to a score function independent of the chosen learning algorithm [ 37].

The selection criterion measures the usefulness of any feature subset, and feature selection seeks the feature subset that optimizes the selection criterion.

Previous studies [44, 45] have shown that the classification type information implied by a feature can be used to evaluate the usefulness of a feature.

Second, for an uncontrolled and highly heterogeneous collection such as the Web, it is difficult to decide which image features better represent image visual content, since the usefulness of each feature may vary according to the type of image.

A low probability of error of individual features as obtained using baysian classifier would indicate good discriminatory ability and asserts the usefulness of the feature.

Other researchers also used ApEn for seizure detection and showed the usefulness of this feature in this regard [ 33– 33].