Sentence examples for a feature evaluation from inspiring English sources

We derive a feature evaluation criterion, named gSide, to estimate the usefulness of subgraph patterns based upon side views.

The mRMR algorithm proposed by Peng et al. [ 30] is a feature evaluation method based on mutual information.

A fuzzy feature evaluation index for a set of features is newly defined in terms of degree of similarity between two patterns in both the original and transformed feature spaces.

We have analysed the task of CoreSC annotation both from a sentence classification as well as sequence labelling perspective and we present a detailed feature evaluation.

So a loose feature evaluation criterion is conducted in preliminary selection.

It is known that dependency is an important feature evaluation measure based on rough set theory.

We show improved results with respect to several other highly competitive local image features through both a classic local feature evaluation procedure and an illustrative application scenario.

CFS is a filter-based subset feature evaluation algorithm that evaluates each feature subset using simple objective functions.

The comparison and analysis of classification complexity are made between the two measures and it is indicated that neighborhood-tolerance conditional entropy is a more effective feature evaluation criterion than dependency in incomplete decision table.

The proposed method is inspired by the spectral feature selection, by using together a kernel and a new spectrum based feature evaluation measure for quantifying the feature relevance.

To further analyze the impact of features on prediction performance and choose an optimized subset, feature evaluation was done by using the select attributes module in Weka 3.6.1 [35] according to the square of the weight assigned by the SVM [36].