Feature Selection: An Ever Evolving Frontier in Data Mining
Huan Liu, Hiroshi Motoda, Rudy Setiono, Zheng Zhao;
JMLR W&P 10:4-13, 2010.
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 effective technique for dimension reduction and an essential step in successful data mining applications. It is a research area of great practical significance and has been developed and evolved to answer the challenges due to data of increasingly high dimensionality. Its direct benefits include: building simpler and more comprehensible models, improving data mining performance, and helping prepare, clean, and understand data. We first briefly 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 field of some contemporary interests, new applications, and ongoing research efforts. We then examine nascent demands in data-intensive applications and identify some potential lines of research that require multidisciplinary efforts.