Approximate polytope ensemble for one-class classification

作者：

Highlights：

• The methodology uses a convex-hull for modeling one-class classification problems.

• Random projections are used to approximate the convex-hull in high dimensional spaces.

• Expansions of the approximate hulls are considered to set the optimal operating point.

• Exhaustive validation is performed on three different typologies of problems.

摘要

Highlights•The methodology uses a convex-hull for modeling one-class classification problems.•Random projections are used to approximate the convex-hull in high dimensional spaces.•Expansions of the approximate hulls are considered to set the optimal operating point.•Exhaustive validation is performed on three different typologies of problems.

论文关键词：One-class classification,Convex hull,High-dimensionality,Random projections,Ensemble learning

论文评审过程：Received 5 November 2012, Revised 22 July 2013, Accepted 4 August 2013, Available online 21 August 2013.

论文官网地址：https://doi.org/10.1016/j.patcog.2013.08.007