Sentence examples for reduced classification from inspiring English sources

The usage of the reduced set of features and SVM classifier gave only slightly reduced classification performances, which did not differ from the full sets of features.

Feature dimension reduction using principal component analysis (PCA) may provide effective classification performance and reduced classification time.

The accuracy of the model was 79.3% and reduced classification errors from 29.8% to 20.7%.

It reduced classification error rates on the ER-positive (11% versus 28%; P = 0.0389), the ER-negative (5% versus 41%; P < 0.0001) and the triple-negative (11% versus 56%; P = 0.1336) subgroups of the PAM50 training cohort.

We demonstrate a framework for selecting feature subsets from all the newly extracted components, leading to reduced classification error rates on the gene expression microarray data.

Additional aberrations were observed in pRCCs, such as gain of chr7 in specimen K-13 (Fig.  2C), but because of their presence in multiple subtypes, inclusion in the tree resulted in reduced classification accuracy.

We observe that the proposed posterior smoothing in the acoustic space can further reduce classification errors.

In the present literature, there exists no other distinct way to optimally select the features without reducing classification performance.

Empirical results confirm that LB can statistically significantly outperform alternative methods in terms of reducing classification error.

On the other hand, having too many regions reduces classification efficiency and may also lower recognition accuracy due to too much unnecessary location information being included.

As a result, LB, which is built on these classifiers, can significantly reduce classification error, compared with the traditional bagging (TB) approach.