Sentence examples for used feature selection algorithm from inspiring English sources

We present a formalism of selective sampling based on data variance, and apply it to a widely used feature selection algorithm Relief.

In this study, we use maximum relevance minimum redundancy (MRMR), which is a frequently used feature selection algorithm.

LASSO is a well-known widely used feature selection algorithm, particularly when the number of samples is considerably smaller than the number of features.

The proposed method is tested using five databases and is shown to outperform many commonly used feature selection algorithms.

Using feature selection algorithm based on rough sets theory, six main indicators were identified as the most effective factors.

Development of the prediction model: We selected predictive descriptors using feature selection algorithms (provided by RapidMiner 5.1.13), which returned 113 descriptors as presumably predictive.

We sought such sets using feature selection algorithms (see Supplementary Methods S1 section).

Among the 52 NIP descriptors used for this investigation, we identified using feature selection algorithms (see Methods) the smallest subsets of descriptors that best discriminate taxa.

The smallest subset of NIP descriptors that still performs as well as the complete set of 52 was identified using feature selection algorithms [ 48, 49] and a heuristic evaluation of subsets of descriptors on the classification models identified earlier as the best ones.

Since MDR algorithm can not be applied to a large dataset directly, we first reduce the number of SNPs to 10 by ReliefF [ 19], a commonly-used feature selection algorithm, and then MDR performs an exhaustive search for a SNP set that can maximize cross-validation consistency and prediction accuracy.

Finally, we look into question c by using feature selection methods such as genetic algorithms (GAs) to obtain classifiers of slightly lower accuracy, working with few features (genes).