Exploiting Monotonicity Constraints in Active Learning for Ordinal Classification

Abstract

We consider ordinal classification and instance ranking problems where each attribute is known to have an increasing or decreasing relation with the class label or rank. For example, it stands to reason that the number of query terms occurring in a document has a positive influence on its relevance to the query. We aim to exploit such monotonicity constraints by using labeled attribute vectors to draw conclusions about the class labels of order related unlabeled ones. Assuming we have a pool of unlabeled attribute vectors, and an oracle that can be queried for class labels, the central problem is to choose a query point whose label is expected to provide the most information. We evaluate different query strategies by comparing the number of inferred labels after some limited number of queries, as well as by comparing the prediction errors of models trained on the points whose labels have been determined so far. We present an efficient algorithm to determine the query point preferred by the well-known active learning strategy generalized binary search. This algorithm can be applied to binary classification on incomplete matrix orders. For non-binary classification, we propose to include attribute vectors in the training set whose class labels have not been uniquely determined yet. We perform experiments on artificial and real data.

Active Learning for Ordinal Classification Based on Adaptive Diversity-Based Uncertainty Sampling

“…Although great progress has been made in active learning research on classification, little effort has been devoted to the problem of active learning for ordinal classification. The active learning algorithm for ordinal classification was first studied by exploiting monotonicity constraints in the data [37]. This approach, however, is only suitable for monotonic classification problems [38], which is a special case of generic ordinal classification problems [38].…”