Sentence examples for better classification accuracy of from inspiring English sources

Our algorithm gives better classification accuracy of almost 5% and 8% than Koza's model and Back propagation model respectively even for complex and real multi-class data in lesser amount of time.

Furthermore, the better classification accuracy of the accelerometer attached to the withers (95%% of minutes classified correctly) compared to the poll attachment (83%% of minutes classified correctly) suggests that the withers may be the preferred site of attachment.

For the English movie review dataset, Nicholls and Song [8] obtained a baseline accuracy of 79.9% with the MEM classifier, and better classification accuracies of 86.9, 85.7, and 80.9% when combined with DFD, CHI2, and OCFS feature selection methods, respectively.

A recent comparison of feature selection methods for multiclass microarray data classification (Chai and Domeniconi, 2004) shows that wrapper methods such as SVM-RFE lead to better classification accuracy for large number of features, but often gives lower accuracy than filter methods when the number of selected features is very small.

For Subject 1 and Subject 3, it is observed that the proposed feature extraction method provides better classification accuracy irrespective of the kernel.

According to Hegde et al. [16], 8 MFCC coefficients, with the use of Fisher's ratio technique, could have better classification accuracy than other number of coefficients 3 to 12 MFCCs for 5 vowels in Kannada language.

A sociopathic knowledge base has the property that all the rules are individually judged to be correct rules, yet a subset of the knowledge base gives better classification accuracy than the original knowledge base, independent of the amount of computational resources that are available.

We have recently compared several types of features using decision tree models and showed that using ChIP-chip data as features generally result in better classification accuracy that using other types of features, when gene expression and ChIP-chip data are obtained from similar conditions (e.g., normal growth conditions) [ 20].

GP's best model resulted in 0.93% better classification accuracy by reducing the dimension n of the feature space from 256 to 15.

Comprehensive experimentation on UCI repository data and face data sets (ORL, CMU, Yale) confirms the superiority of SubXPCA with better classification accuracy.

It enables better classification accuracy and works with a manageable number of features that can be extracted from both Minkowski functionals and finally leads to interpretable models.