A minimax probability extreme machine framework and its application in pattern recognition. (May 2019)

• Record Type: Journal Article
• Authors: Yang, Liming; Yang, Boyan; Jing, Shibo; sun, Qun
• Is Part Of: Engineering applications of artificial intelligence. Volume 81(2019)
• Page Start: 260
• Page End: 269
• Publication Date: 2019-05
• Subjects: Minimax probability machine -- Extreme learning machine -- Empirical risk -- Second-order cone programming -- Chebyshev–Cantelli inequality; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.02.012
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In this work we propose a minimax probability extreme learning machine framework (MPME), which combines the benefits of minimax probability machine (MPM) with extreme learning machine (ELM). For binary classification problems, we illustrate that the proposed MPME can be interpreted geometrically by minimizing the maximum of Mahalanobis distances to the two classes. Then two variants of the MPME are presented based on the l 2 -norm loss and l 1 -norm loss functions (called LSEMPME and LADMPME) respectively. Without making specific assumption on the data distribution, the proposed methods can provide explicit upper-bounds for the generalization error, moreover the LSEMPME and LADMPME minimize empirical risk simultaneously. The decision hyperplanes of the proposed methods pass through the origin in ELM feature space with few decision variables. By using the multivariate Chebyshev–Cantelli inequality, all the proposed problems can be reformulated as second-order cone programming (SOCP) with global solutions. Furthermore, numerical experiments have been carried out on two databases that are drawn from UCI benchmark database and a practical application database. First, the proposed methods are evaluated for a practical application consisting on the analysis of licorice seeds using near-infrared spectral (NIR) data. Experiments in six different spectral regions illustrate that the proposed methods can improve generalization in most cases. Then the proposed methods are evaluated on benchmark datasets. In comparison with traditional methods including MPM, ELM and support vector machine (SVM), experiments show that the proposed methods achieve comparable results in generalization. With few decision variables, the proposed methods are easy to implement for nonlinear classification and to estimate a lower-bound on the prediction accuracy. Highlights: We propose a new minimax probability extreme learning machine (MPME). We extend MPME based on 1-norm and 2-norm loss functions respectively. All problems are posed as SOCP problems with global solutions. MPME can provide an explicit lower bound on correct classification probability. MPME has good interpretation geometrically in ELM feature space.