Sentence examples for using the feature subset from inspiring English sources

An SVM model for meter classification is built on each training split using the feature subset.

Additionally, each food source stores its quality (fitness), which is given by the accuracy of the classifier using the feature subset indicated by the bit vector.

Differentially expressed genes in tumours with high levels of HSF1 were identified using the feature subset selection (FSS) method (P<0.05, fold >1.5) when high HSF1 was defined as immunohistochemistry (IHC) staining index 9 (upper tertile), and SAM (significance analyses of microarray) when defining high HSF1 as upper tertile of mRNA values (P<0.001, FDR <0.001).

Differentially expressed genes for ER α expressing tumours according to GPER status (ER α+/GPER+ vs ER α+/GPER−) were identified using the Feature subset selection (FSS) method (P<0.05, fold>1.5) from 59 (internal set) and 58 (external set) ER α+ patients.

The classifier constructed using the optimal feature subset achieved 80.63% prediction strength with MCC = 0.6310 and AUC = 0.8961.

Table  3 shows the results for the ability of the BANN-trained classifier, developed using the selected feature subset, to distinguish between BRCA1/2 mutation carriers and non-carriers in the independent testing dataset.

Most MOEAs use the cardinality of the feature subset as one of its objectives and adopt a strict Pareto dominance relationship to select individuals.

Submit a feature subset of food sources to the classifier and use accuracy as fitness: the feature subset of each food source is submitted to the classifier, and accuracy is stored as the fitness of food source.

4. Submit a feature subset of neighbors to the classifier and use accuracy as fitness: the feature subset created for each neighbor is submitted to the classifier, and accuracy is stored as the neighbor's fitness.   5.

So besides the integration of known biomarkers from the literature, fsCoP may also be used to further refine the feature subset selected by the existing feature selection algorithms.

To further assess the performance of cost-sensitive ensemble algorithm, four ensemble algorithms, namely, Direct Combination, EasyEnsemble [ 51], BalanceCascade [ 51], and SMOTEBoost [ 52], are adopted in this paper using the optimal feature subsets.