Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer.

ABSTRACT: Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeate

SUBMITTER: Basavanhally A 

PROVIDER: S-EPMC4436385 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature