seqest (version 1.0.1)

update_data_ord: Add the new sample into labeled dataset from unlabeled dataset for the ordinal case

Description

update_data_ord selects the sample to the labeled dataset according to it's index

Usage

update_data_ord(ind, splitted, data, train, labeled_ids, unlabeled_ids)

Arguments

ind

A numeric value denotes the index of selected sample.

splitted

A list containing the datasets which we will use in the cordinl case. Note that the element of the data_split is the samples from Classes k-1and Classes k

data

A matrix denotes all the data including the labeled samples and the unlabeled samples. Note that the first column of the dataset is the response variable, that's the labels and the rest is the explanatory variables.

train

A matrix for the labeled samples.

labeled_ids

The unique identification of the labeled dataset

unlabeled_ids

The unique identification of the unlabeled dataset

Value

splitted

a list containing the new datasets which we add a new sample into it

newY

the label of the choosed sample

train

the dataset used for training the model after adding the new sample

labeled_ids

the id of the labeled dataset after updating

unlabeled_ids

the id of the unlabeled dataset after updating

Details

update_data_ord chooses the sample based on the index from all the training ordinal dataset. We record the corresponding label of the selected sample and update the data of the unlabeled dataset and the labeled dataset. Specifically, we remove the index of the choosed sample from the unlabeled dataset and add the sample to the labeled dataset.

References

Li, J., Chen, Z., Wang, Z., & Chang, Y. I. (2020). Active learning in multiple-class classification problems via individualized binary models. Computational Statistics & Data Analysis, 145, 106911. doi:10.1016/j.csda.2020.106911

See Also

update_data_cat

Examples

# NOT RUN {
## For an example, see example(seq_ord_model)
# }