Research on Highly Skewed Propeller

Performance of non-parametric classifiers on highly skewed data

A wide range of non-parametric classifiers have been suggested and developed in the recent years in order to overcome the compulsion of using the classical parametric Maximum Likelihood Classifier (MLC) for non-normal data. The most advanced of these classifiers being the ones based on the Artificial Neural Network (ANN) algorithm, the Support Vector Machines (SVMs) and the Random Forests (RFs)...

Robust Learning with Highly Skewed Category Distributions

Highly skewed category distributions are abundant in many real-world tasks in data mining, such as medical diagnosis (rare diseases), text categorization (rare topics), and fraud detection (when most transactions are legitimate). Under extreme class skew, most supervised learning algorithms tend to minimize loss by labeling every instance with the majority class(es), leading to poor recall on t...

Why is Platform Pricing Generally Highly Skewed?

Bolt and Tieman (2008) suggested that profit function non-concavities may account for the prevalence of skewed pricing by two-sided platform businesses. In the Rochet-Tirole (2003) model, however non-concavity is not necessary for highly skewed pricing. Ubiquitous high pass-through rates are sufficient but implausible. In the Armstrong (2006) model, non-concavity is neither necessary nor suffic...

Shearing Properties of the Skewed Woven Fabrics (RESEARCH NOTE)

Biased Skip Lists for Highly Skewed Access Patterns

1 Dynamic tables that support search, insert and delete operations are fundamental and well studied in computer science. There are many well known data structures that solve this problem, including balanced binary trees, skip lists and tries among others. Many of the existing data structures work efficiently when the access patterns are uniform, but in many circumstance access patterns are bias...