A Robust Active Learning Framework Using Itemset Based Dynamic Rule Sampling

Active learning is a rapidly growing field of machine learning which aims at reducing the labeling effort of the oracle (human expert) in acquiring informative training samples in domains where the cost of labeling is high. Associative classification is a well established prediction method which possesses the advantages of high accuracy and faster learning rates in classification. In this paper, we propose a novel algorithm which unifies associative classification with active learning. The algorithm has two major procedures of Rule generation and rule pruning. The algorithm selects unlabeled instances from the pool of available samples and uses a unique dynamic rule sampling procedure for updating the model. The rules are dynamically sampled class association rules (CAR) which are generated using the mined Minimal infrequent itemsets. The results derived over 10 datasets from the UCI-ML repository for our approach have been compared with those from the ACTIVE-DECORATE algorithm. We also analyze our sampling method against the state of art sampling frameworks and show that our method performs better.