In real applications of inductive learning, labeled instances are often deficient. The countermeasure is either to ask experts to label informative instances in active learning, or to borrow useful information from abundant labeled instances in the source domain in transfer learning. Due to the high cost of querying experts, it is promising to integrate the two methodologies into a more robust and reliable classification framework to compensate the disadvantages of both methods. Recently, a few research studies have been investigated to integrate the two methods together, which is called transfer active learning. However, when there exist unrelated domains which have different distributions or label assignments, an inevitable problem named negative transfer will happen which leads to degenerated performance. Also, how to avoid selecting unconcerned samples to query is still an open question. To tackle these issues, we propose a hybrid algorithm for active learning with the help of transfer learning by adopting a divergence measure to measure the similarities between different domains, so that the negative effects can be alleviated. To avoid querying irrelevant instances, we also present an adaptive strategy that is able to eliminate unnecessary instances in the input space and models in the model space. Extensive experiments on both synthetic and real data sets show that our algorithm is able to query less instances and converges faster than the state-of-the-art methods.