The development of sparse techniques presents a major challenge to complex nonlinear high-dimensional data. In this paper, we propose novel feature selection method for support vector regression, called FS-NSVR, which first attempts solve the problem in regression technology field. FS-NSVR preserves representative features selected system due its use matrix original space. is challenging mixed-...

Faulty compressors must be detected in advance to speed up the quality control process of compressor's performance. Machine learning models have recently been used as fault classification distinguish between normal and abnormal compressors, facilitating more sophisticated detection methods than those past. However, very few studies conducted on accurate efficient feature selection, despite its ...

Heart disease has become a non-ignorable threat to human health in recent years. Once without timely diagnosis and treatment, patients often suffer disability or even death. However, the accuracy directly relies on different doctors’ experiences various factors associated with heart bring heavy tasks them make situation worse. Therefore, improve introducing computer-aided techniques assist doct...

Sparse learning based feature selection has been widely investigated in recent years. In this study, we focus on the l2,0-norm selection, which is effective for exact top-k but challenging to optimize. To solve general constrained problems, novelly develop a parameter-free optimization framework coordinate descend (CD) method, termed CD-LSR. Specifically, devise skillful conversion from origina...

Multi-label feature selection attracts considerable attention from multi-label learning. Information theory-based methods intend to select the most informative features and reduce uncertain amount of information labels. Previous regard labels as constant. In fact, classification label set is captured by features, remaining uncertainty each changing dynamically. this paper, we categorize into tw...