Simultaneous Feature Selection and Classification via Semi-Supervised Models
Liming Yang, Lai‐Sheng Wang · 2007
Feature selecting for semi-supervised support vector machines (S3VM) classifiers is a novel and important research subject in machine learning. For this problem, based on the linear semi-supervised support vector machine (S3VM) with 1-norm, we propose two new models using all the available data from labeled and unlabeled data and also utilizing as few of the useful features as possible. Furthermore, two proposed learning models for simultaneous feature selection and S3VM classification can be converted to the minimization concave function on the polyhedral set and then solved using successive linear approximation algorithms. Experiments on publicly available datasets prove the effective of our models compared with the linear S3VM model for 1-norm.