Samuel Schrader

Title: Samuel Schrader: Innovator in Data Oversampling Techniques

Introduction

Samuel Schrader is an accomplished inventor based in Irvine, CA. He has made significant contributions to the field of data science, particularly in addressing challenges associated with imbalanced datasets. His innovative approach has garnered attention in both academic and industry circles.

Latest Patents

Samuel Schrader holds a patent titled "Local-adapted minority oversampling strategy for highly imbalanced highly noisy dataset." This patent focuses on applying a local-adapted minority oversampling strategy technique to an imbalanced dataset that includes positive samples belonging to a minority class and negative samples belonging to a majority class. The minority class is less prevalent than the majority class, and each sample from the dataset includes a plurality of features. The strategy involves determining a local imbalance for each positive sample from the minority class, calculating a local-adapted oversampling ratio, and replicating each positive sample to generate a new dataset.

Career Highlights

Samuel Schrader is currently employed at Canon U.S.A., Inc., where he continues to develop innovative solutions in data analysis and machine learning. His work has been instrumental in enhancing the performance of algorithms used in various applications.

Collaborations

Samuel has collaborated with Xiwu Cao, a fellow innovator, to further advance their research and development efforts in the field of data science.

Conclusion

Samuel Schrader's contributions to the field of data oversampling techniques highlight his innovative spirit and dedication to solving complex problems. His work continues to influence the way data is analyzed and utilized across various industries.