Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

Survival analysis is a subfield of statistics where the goal is to analyze and model data
where the outcome is the time until an event of interest occurs. One of the main challenges …

Nowadays, the growth of the high-throughput technologies has resulted in exponential
growth in the harvested data with respect to both dimensionality and sample size. The trend …

Feature selection (FS) is an important component of many pattern recognition tasks. In these
tasks, one is often confronted with very high-dimensional data. FS algorithms are designed …

We're surrounded by huge amounts of large-scale high-dimensional data, but learning tasks
require reduced data dimensionality. Feature selection has shown its effectiveness in many …

Non-convex sparsity-inducing penalties have recently received considerable attentions in
sparse learning. Recent theoretical investigations have demonstrated their superiority over …

The understanding of web images has been a hot research topic in both artificial intelligence
and multimedia content analysis domains. The web images are composed of various …

Feature selection is important in both machine learning and pattern recognition.
Successfully selecting informative features can significantly increase learning accuracy and …

Organizations in many domains generate a considerable amount of heterogeneous data
every day. Such data can be processed to enhance these organizations' decisions in real …

Supplying a comprehensive overview of healthcare analytics research, Healthcare Data
Analytics provides an understanding of the analytical techniques currently available to solve …