Abstract:

There is a basic assumption in many existing data mining and machine learning techniques，that training and test data must be governed by the same distribution.However，this assumption does not hold in many cases，then traditional machine learning methods not aware of the difference of distribution may fail.This paper proposes a novel transfer-learning algorithm called DRTAT，which dynamically regroups the primary training data sets and eliminates the redundancy data timely，then makes classifiers ensemble.The experiments are performed on many text data sets and the UCI benchmark data sets，and DRTAT is compared with TrAdaboost algorithm，the results show the superiority of DRTAT.

摘要： 目前很多数据挖掘和机器学习方法都有一个基本假设：训练数据和测试数据必须服从相同的分布。但是在很多情况下这种假设不成立，没有考虑分布差异的传统机器学习方法就不能正确分类了。提出了一种新的迁移学习方法DRTAT，对原训练数据进行动态分割重组，适时地淘汰冗余数据，并进行分类器的集成。通过在多个文本数据集和UCI数据集上进行测试，并与TrAdaboost算法进行比较，表明了算法的先进性。