Adaptive Boosting for Transfer Learning Using Dynamic Updates

Instance-based transfer learning methods utilize labeled examples from one domain to improve learning performance in another domain via knowledge transfer. Boosting-based transfer learning algorithms are a subset of such methods and have been applied successfully within the transfer learning community. In this paper, we address some of the weaknesses of such algorithms and extend the most popular transfer boosting algorithm, TrAdaBoost. We incorporate a dynamic factor into TrAdaBoost to make it meet its intended design of incorporating the advantages of both AdaBoost and the "Weighted Majority Algorithm". We theoretically and empirically analyze the effect of this important factor on the boosting performance of TrAdaBoost and we apply it as a "correction factor" that significantly improves the classification performance. Our experimental results on several real-world datasets demonstrate the effectiveness of our framework in obtaining better classification results.