Identifying Mislabeled Training Data

This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classification accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to create classifiers that serve as noise filters for the training data. We evaluate single algorithm, majority vote and consensus filters on five datasets that are prone to labeling errors. Our experiments illustrate that filtering significantly improves classification accuracy for noise levels up to 30 percent. An analytical and empirical evaluation of the precision of our approach shows that consensus filters are conservative at throwing away good data at the expense of retaining bad data and that majority filters are better at detecting bad data at the expense of throwing away good data. This suggests that for situations in which there is a paucity of data, consensus filters are preferable, whereas majority vote filters are preferable for situations with an abundance of data

Antitrust Standing in Private Merger Cases: Reconciling Private Incentives and Public Enforcement Goals

This article examines a vital problem of private antitrust enforcement - the standing of private merger litigants - where the unresolved tension between public antitrust goals and the private interests of litigants threatens enforcement breakdown. Private merger enforcement is at risk not because courts have determined that such enforcement is undesirable, but because courts have failed to see the problem as an issue of systems design requiring effective integration of public and private enforcement. Instead they have focused on particular elements of antitrust standing - feared abuses by wrongly motivated plaintiffs - neglecting system-wide effects and jeopardizing the health of private enforcement as a whole. In this paper, I attempt to develop a coherent method for reconciling public interest goals and private enforcement incentives that will be useful not only for merger enforcement, but for other areas of antitrust law, and perhaps for public interest litigation generally

The Search for Limits and the Quest for Theory (Introduction to The Deregulation of Industry Colloquium)

Colloquium: The Deregulation of Industr