This paper introduces a Bayesian algorithm for constructing predictive models from data that are optimized to predict a target variable well for a particular instance. This algori...
The goal of this paper is to improve the prediction performance of fault-prone module prediction models (fault-proneness models) by employing over/under sampling methods, which ar...
Code churn, the amount of code change taking place within a software unit over time, has been correlated with fault-proneness in software systems. We investigate the use of code c...
Lucas Layman, Gunnar Kudrjavets, Nachiappan Nagapp...
Background: Intrinsically disordered proteins play important roles in various cellular activities and their prevalence was implicated in a number of human diseases. The knowledge ...
Marcin J. Mizianty, Tuo Zhang, Bin Xue, Yaoqi Zhou...
This work studies the impact of using dynamic information as features in a machine learning algorithm for the prediction task of classifying critically ill patients in two classes ...