Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Richard A. Bauder"'
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-31 (2023)
Abstract As a means of building explainable machine learning models for Big Data, we apply a novel ensemble supervised feature selection technique. The technique is applied to publicly available insurance claims data from the United States public hea
Externí odkaz:
https://doaj.org/article/89c068cfa9654be2a0a8a7368f4bcc4f
Publikováno v:
Journal of Big Data, Vol 7, Iss 1, Pp 1-19 (2020)
Abstract A majority of predictive models should be updated regularly, since the most recent data associated with the model may have a different distribution from that of the original training data. This difference may be critical enough to impact the
Externí odkaz:
https://doaj.org/article/438e8c7312c244908f9c1b2c8ad702b0
Publikováno v:
Journal of Big Data, Vol 7, Iss 1, Pp 1-17 (2020)
Abstract In Machine Learning, if one class has a significantly larger number of instances (majority) than the other (minority), this condition is defined as class imbalance. With regard to datasets, class imbalance can bias the predictive capabilitie
Externí odkaz:
https://doaj.org/article/8d3902a1a33d48b4a2eba30a572b826e
Publikováno v:
Journal of Big Data, Vol 6, Iss 1, Pp 1-25 (2019)
Abstract Severe class imbalance between majority and minority classes in Big Data can bias the predictive performance of Machine Learning algorithms toward the majority (negative) class. Where the minority (positive) class holds greater value than th
Externí odkaz:
https://doaj.org/article/456a31666b5843de9e50fcd6794b909e
Publikováno v:
Journal of Big Data, Vol 6, Iss 1, Pp 1-33 (2019)
Abstract The United States healthcare system produces an enormous volume of data with a vast number of financial transactions generated by physicians administering healthcare services. This makes healthcare fraud difficult to detect, especially when
Externí odkaz:
https://doaj.org/article/59bc88873ec84c7180c92f68493f5b1c
Publikováno v:
Journal of Big Data, Vol 5, Iss 1, Pp 1-30 (2018)
Abstract In a majority–minority classification problem, class imbalance in the dataset(s) can dramatically skew the performance of classifiers, introducing a prediction bias for the majority class. Assuming the positive (minority) class is the grou
Externí odkaz:
https://doaj.org/article/5195fba8636147bd9df125f67f3f4606
Publikováno v:
Journal of Big Data, Vol 5, Iss 1, Pp 1-21 (2018)
Abstract In the United States, advances in technology and medical sciences continue to improve the general well-being of the population. With this continued progress, programs such as Medicare are needed to help manage the high costs associated with
Externí odkaz:
https://doaj.org/article/3a30ebad615e46278f96366d7aa6ee31
Publikováno v:
Journal of Big Data, Vol 7, Iss 1, Pp 1-17 (2020)
In Machine Learning, if one class has a significantly larger number of instances (majority) than the other (minority), this condition is defined as class imbalance. With regard to datasets, class imbalance can bias the predictive capabilities of Mach
Publikováno v:
Intelligent Data Analysis. 24:141-161
Publikováno v:
Journal of Big Data, Vol 7, Iss 1, Pp 1-19 (2020)
A majority of predictive models should be updated regularly, since the most recent data associated with the model may have a different distribution from that of the original training data. This difference may be critical enough to impact the effectiv