Jennifer G. Dy

TL;DR: This paper explores the feature selection problem and issues through FSSEM (Feature Subset Selection using Expectation-Maximization (EM) clustering) and through two different performance criteria for evaluating candidate feature subsets: scatter separability and maximum likelihood.

TL;DR: This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson's disease, and a support vector machine (SVM) classifier was implemented to estimateThe severity of tremor, bradykinesia and dyskinesian symptoms from accelerometers data features.

TL;DR: This fully automated algorithm diagnosed plus disease in ROP with comparable or better accuracy than human experts, which has potential applications in disease detection, monitoring, and prognosis in infants at risk of ROP.

TL;DR: It is shown that imputation with the tested methods on average improves classification accuracy when compared to classification without imputation, and some classifiers such as C4.5 and Nai@?ve-Bayes were found to be missing data resistant, i.e., they can produce accurate classification in the presence of missing data.

TL;DR: In this article, the authors employ a probabilistic model for learning from multiple annotators that can also learn the annotator expertise even when their expertise may not be consistently accurate across the task domain.