By examining the classification rate within the tumor subgroups, it is evident that our predictive gene sets tend to perform better in ER positive, Stage I and II, and node negative tumors.
The estimation accuracy and the generalization ability are evaluated by the classification rates and the associated receiver-operating-characteristic (ROC) curves in cross validations.
Compared with non-block based methods, the proposed block-based method could increase the classification rate by 5%10%%.
Results indicate that both for artificial and real datasets the classification rate achieved by the model largely outperformed that of KNN.
The improvement of the classification rate obtained by V-ELM over the original ELM is 2.06%, 2.48%, and 2.66% with the linear kernel, the sigmoid kernel, and the Gaussian kernel, respectively.
From Tables 1 and 3, it is interesting to see that although the number of samples used from training dataset is the same, the classification rate obtained by using randomly generated protein sequence training dataset is higher than the one by fixing the PIR1 as the training dataset.
As compared to [1], the classification rate is improved by up to 22%% while the running time and memory requirements are saved for 18 and 50%%, respectively.
These experiments confirm the interest of PMMs and TMMs in the time series modeling: specifically, the classification rate can be improved by nearly fifty percent.
In Chappaqua, one of the county's wealthiest districts, the classification rate has been traditionally low.
As illustrated in these two figures, the classification rates obtained by V-ELM gradually increase when the used ensembles are increased from 5 to 15.
