Combining Committee-Based Semi-supervised and Active Learning and Its Application to Handwritten Digits Recognition

Semi-supervised learning

reduces the cost of labeling the training data of a supervised learning algorithm through using unlabeled data together with labeled data to improve the performance.

Co-Training

is a popular

semi-supervised learning

algorithm, that requires multiple redundant and independent sets of features (views). In many real-world application domains, this requirement can not be satisfied. In this paper, a single-view variant of

Co-Training

,

CoBC

(Co-Training by Committee), is proposed, which requires an ensemble of diverse classifiers instead of the redundant and independent views. Then we introduce two new learning algorithms,

QBC-then-CoBC

and

QBC-with-CoBC

, which combines the merits of committee-based

semi-supervised learning

and committee-based

active learning

. An empirical study on handwritten digit recognition is conducted where the random subspace method (

RSM

) is used to create ensembles of diverse C4.5 decision trees. Experiments show that these two combinations outperform the other non committee-based ones.