Extreme logistic regression

With the recent popularity of electronic medical records, enormous amount of medical data is being generated every day at an exponential rate. Machine learning methods have been shown in many studies to be capable of producing automatic medical diagnostic models such as automated prognostic models. However, many powerful machine learning algorithms such as support vector machine (SVM), Random Forest (RF) or Kernel Logistic Regression (KLR) are unbearably slow for very large datasets. This makes their use in medical research limited to small to medium scale problems. This study is motivated by an ongoing research on prostate cancer mortality prediction for a national representative of US population where the SVM and RF took several hours or days to train whereas simple linear methods such as logistic regression or linear discriminant analysis take minutes or even seconds. Because, most real-world problems are non-linear, this paper presents a large scale algorithm enabling a recently p...

• Type: Journal
• Year: 2016
• Where: ADAC
• Authors: Che Ngufor, Janusz Wojtusiak
• Keywords: ADAC 2016, Theoretical Computer Science