title: Improvements on ν-Twin Support Vector Machine.
abstract: In this paper, we propose two novel binary classifiers termed as "Improvements on ν-Twin Support Vector Machine: Iν-TWSVM and Iν-TWSVM (Fast)" that are motivated by ν-Twin Support Vector Machine (ν-TWSVM). Similar to ν-TWSVM, Iν-TWSVM determines two nonparallel hyperplanes such that they are closer to their respective classes and are at least ρ distance away from the other class. The significant advantage of Iν-TWSVM over ν-TWSVM is that Iν-TWSVM solves one smaller-sized Quadratic Programming Problem (QPP) and one Unconstrained Minimization Problem (UMP); as compared to solving two related QPPs in ν-TWSVM. Further, Iν-TWSVM (Fast) avoids solving a smaller sized QPP and transforms it as a unimodal function, which can be solved using line search methods and similar to Iν-TWSVM, the other problem is solved as a UMP. Due to their novel formulation, the proposed classifiers are faster than ν-TWSVM and have comparable generalization ability. Iν-TWSVM also implements structural risk minimization (SRM) principle by introducing a regularization term, along with minimizing the empirical risk. The other properties of Iν-TWSVM, related to support vectors (SVs), are similar to that of ν-TWSVM. To test the efficacy of the proposed method, experiments have been conducted on a wide range of UCI and a skewed variation of NDC datasets. We have also given the application of Iν-TWSVM as a binary classifier for pixel classification of color images.
authors.affiliation: Department of Computer Science, Faculty of Mathematics and Computer Science, South Asian University, Delhi, India. Electronic address: reshma.khemchandani@sau.ac.in.
authors.forename: Reshma
authors.initials: R
authors.lastname: Khemchandani
authors.affiliation: Department of Computer Science, Faculty of Mathematics and Computer Science, South Asian University, Delhi, India. Electronic address: pooja.saigal@students.sau.ac.in.
authors.forename: Pooja
authors.initials: P
authors.lastname: Saigal
authors.affiliation: Department of Mathematics, Indian Institute of Technology, Delhi, India. Electronic address: chandras@maths.iitd.ac.in.
authors.forename: Suresh
authors.initials: S
authors.lastname: Chandra
mesh_terms: D000465:Algorithms; D003116:Color; D003118:Color Perception; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D060388:Support Vector Machine / Q000639:trends*
keywords: Image pixel classification; Machine learning; Twin support vector machines; Unconstrained optimization