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Lastly, a data segmentation based support vector machine classification method is proposed, which eliminates the classification error caused by unreliable EEG data segments.

In Florida, for example, the state categorically eliminated the classification of "independent contractor" in the construction trades, for the purpose of worker's compensation compliance.

The objective function in Eq. (5), which is utilized to quantify searched solutions, is defined by dividing the classification error by the number of eliminated features.

We therefore use Reduced Error Pruning, which consists of recursively eliminating subtrees that do not improve the classification error on the pruning set (which only contains individuals that were not used during training).

Table 2 presents details of the classification errors.

We use the fitness function shown below: (7)fitness = error + γ *number_of_selected_features, where error corresponds to the classification error on the validation data set D v, γ is a trade-off between classification error and the number of selected features.

The classification error rate of this model was reported.

Classification performance was then evaluated on only the left-out test set, to ensure unbiased estimation of the classification error.

Five hundred bootstrapped iterations are performed to obtain an estimate of the classification error rate.

The resulting set was used for classification and determination of the classification error, both as OOB error and as average error, on all gene expression sets.

Although there is still a potential for subject input error, this method eliminates the errors of secondary data entry.

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