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At step (11) we select the next query as the instance with the minimum d-Confidence.

Least-confident sampling: involves choosing the instance with the least probability of belonging to the class with the most probability.

Query-By-Committee [38], for example, uses a set of classifiers to identify the instance with the highest disagreement.

This drift towards unexplored regions and unknown classes is achieved by selecting the instance with the lowest d-Confidence as the next query.

Margin sampling: involves choosing the instance with the least margin between the class with most probability and the one with the secondmost probability.

The three metrics, based on the work of Settles [18] and Friedman et al. [12], are described below: 1. Least-confident sampling: involves choosing the instance with the least probability of belonging to the class with the most probability.

The instances with the same labels are forced to be neighbors while separating the instances with different labels.

The paper comprises a case study of online examination system for validating the instances with RDS.

The wrapper implements a new way to relate the instances with the class labels of the bags.

(If error rate > threshold then update the instances with preceding acknowledged classes).

In this paper, users were filtered to include only the instances with active users.