Sentence examples for instance selection methods from inspiring English sources

The taxonomy allows discrimination among a large number of methods which pre-existing taxonomies for offline instance selection methods did not distinguish.

In addition, in contrast to most of the existing methods, which applied instance selection to classification tasks (discrete prediction), the proposed approach is used to obtain instance selection methods for regression tasks (prediction of continuous values).

It uses meta-learning to select the best suited instance selection method for each specific database, among several methods available.

In this context, it is envisaged to use, for instance, feature selection methods in order to select subsets of attributes for the individual classifiers.

This paper presents a refined taxonomy of instance selection and generation methods for the classification of data streams subject to concept drift.

In this paper, a new method called Random Subspace Instance Selection (RSIS) is proposed for the robust design of MIL ensembles without any prior assumptions on the data structure and the proportion of instances in bags.

Although machine learning methods are better targeted at individual-level prediction making, the feature selection methods would also benefit from more stratified options, for instance, in terms of enabling phenotype-specific genetic features, rather than assuming that all subjects share the same panel of predictive genotypes.

Our proposed method can be applied after performing feature selection and instance selection to reduce their dimensionalities and data samples, respectively.

Traditional feature selection methods resort to random sampling in dealing with data sets with a huge number of instances.

Many other selection methods are superior.

Academies have been accused of using selection methods to attract the children likeliest to succeed.