Daniel R Weinberger
Thinking About Schizophrenia in an Era of Genomic Medicine
The American journal of psychiatry
spectral preprocessing techniques certain extent eliminate irrelevant information current noise stray light spectral data thereby enhancing performance prediction models however current preprocessing techniques mostly attempt find best single preprocessing method combination overlooking complementary information among different preprocessing methods preprocessing techniques fail maximize utilization useful information spectral data restrict performance prediction models study proposed spectral ensemble preprocessing method based rapidly developing ensemble learning methods recent years ridge regression rr model named stacking preprocessing ridge regression sprr address aforementioned issues different conventional ensemble learning methods proposed sprr method applied multiple different preprocessing techniques original spectral data generating multiple preprocessed datasets datasets individually inputted rr base models training ultimately rr still served metamodel integrating output results rr base model stacking approach produced diversity base models also achieved higher accuracy lower computational complexity using single type base model apple spectral dataset collected team correlation analysis showed significant complementary information among data produced different preprocessing techniques provided robust theoretical support proposed sprr method introducing currently popular averaging ensemble preprocessing method comparative experiment results applying proposed sprr method six datasets apple meat wheat olive oil tablet corn demonstrated compared single preprocessing method averaging ensemble preprocessing method sprr yielded best accuracy reliability six datasets furthermore conditions training test datasets proposed sprr method demonstrated better performance four commonly used ensemble preprocessing methods