Sample datasets are often collected from different times, places or devices. Due to the existence of the disturbance,
noise and missing data, the collected datasets can not always keep the same distribution, and can even sometimes be required
to concentrate them to reduce the computational burden, which can do the domain adaptation as the preprocessing step
for the sample dataset before being fed into the next step. In order to achieve the above goal, a novel adaptive reduced set
density estimator(A-RSDE) is proposed for adaptive probability density estimation by making full use of the source domain’s
(training dataset) knowledge of the probability density distribution, which lets the target domain’s (testing dataset) probability
density estimation be closer to the true probability density distribution. Meanwhile, the fast core-sets based minimum
enclosing ball(MEB) approximation algorithm is introduced to develop the proposed algorithm. Finally, the experiment
on the benchmark data sets and UCI data sets show that the proposed method has better performance.