Feature Selection: An Ever Evolving Frontier in Data Mining 论文

摘要

The rapid advance of computer technologies in data processing, collection, and storage has provided unparalleled opportunities to expand capabilities in production, services, communications, and research. However, immense quantities of high-dimensional data renew the challenges to the state-of-the-art data mining techniques. Feature selection is an eective technique for dimension reduction and an essential step in successful data mining applications. It is a research area of great practical signicance and has been developed and evolved to answer the challenges due to data of increasingly high dimensionality. Its direct benets include: building simpler and more comprehensible models, improving data mining performance, and helping prepare, clean, and understand data. We rst briey introduce the key components of feature selection, and review its developments with the growth of data mining. We then overview FSDM and the papers of FSDM10, which showcases of a vibrant research eld of some contemporary interests, new applications, and ongoing research eorts. We then examine nascent demands in data-intensive applications and identify some potential lines of research that require multidisciplinary eorts.