Feature selection is one of the key problems for machine learning and data mining. In this
review paper, a brief historical background of the field is given, followed by a selection of …

A novel non-parallel hyperplane Universum support vector machine (U-NHSVM) is
proposed in this paper. Universum data with ensconced prior knowledge are exploited by a …

Penalized regression models such as the lasso have been extensively applied to analyzing
high-dimensional data sets. However, due to memory limitations, existing R packages like …

The-regularized logistic regression (L1-LR) is popular for classification problems. To
accelerate its training speed for high-dimensional data, techniques named safe screening …

Elastic net support vector machine (ENSVM) is an effective and popular classification
technique. It has been widely used in many practical applications. However, solving large …

The problem of learning a sparse model is conceptually interpreted as the process of
identifying active features/samples and then optimizing the model over them. Recently …

Sparse support vector machine (SVM) is a popular classification technique that can
simultaneously learn a small set of the most interpretable features and identify the support …

In this paper we study predictive pattern mining problems where the goal is to construct a
predictive model based on a subset of predictive patterns in the database. Our main …

Multi-label rank support vector machine (RankSVM) is an effective technique to deal with
multi-label classification problems, which has been widely used in various fields. However, it …

L 1 logistic regression is an efficient classification algorithm. Leveraging on the convexity of
the model and the sparsity of L 1 norm, techniques named safe screening rules can help …