Sentence examples for datasets classification from inspiring English sources

The proposed algorithm is demonstrated for two image datasets classification problems.

On private datasets, classification accuracies of as high as 100% have also been reported [33, 35].

The comparison is carried out for different datasets, classification algorithms, and success measures [34].

In case of Accute1, Accute2 and Abalone datasets classification accuracy of EFS-MI is 100% for features numbered 4, 4 and 5, respectively for the classifiers viz.

On such unbalanced datasets, classification accuracy can be misleading in terms of providing a reliable and useful assessment of the performance of the classifier.

Due to the requirement for large datasets, classification methods based on parsimony and likelihood trees typically applied on Sanger-sequenced full-length 16S rRNA genes are not feasible.

Our method aims at learning generalized feature representation for effective cross-dataset classification.

Table 1 Classification accuracy of the five algorithms applied with Stanford Twitter dataset Classification method Classification accuracy L 69.1 LN 72.6 LNS 63.1 LNW 77.3 LNWS 72.7.

Open image in new window Fig. 4 Comparison of trained and tested dataset classification using probabilistic neural networks.

On Heart dataset, classification accuracy found on the proposed method is comparatively low with random forest, KNN and SVM classifier.

Three of these EFCs represent the state-of-the-art of the main approaches to the evolutionary generation of fuzzy rule-based systems for imbalanced dataset classification.