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- Title
An Effective Feature Selection and Classification Model for High Dimensional Big Data Sets.
- Authors
Dingkun Li; Ryu, Keun Ho; Batbaatar, Erdenebileg; Park, Hyun Woo; Jeone, Seon Phil; Zhou Ye
- Abstract
Never before in history is the data growing at such a high volume, variety and velocity. It not only provides multi-sources of information but also generates high dimensional data sets. As the dimensionality of the input data space (i.e., the number of predictors) increases, it becomes exponentially more difficult to find global optima for the parameter space, i.e., to fit models. This is one phenomenon of the “curse of dimensionality". Intuitively, data reduction provides a promising way, feature selection is a one method of data reduction. This paper proposes an effective model for feature selection and classification. It takes advantage of the divide-and-conquer idea combined with MapReduce paradigm to handle high dimensional big data sets. Several well-known classification methods have been used and compared in our experiment and the result illustrates that the proposed model outperforms all selected methods. And this model shows great power and flexibility for handling non-big data as well.
- Subjects
BIG data; DATA analysis; ELECTRONIC data processing
- Publication
International Journal of Design, Analysis & Tools for Integrated Circuits & Systems, 2018, Vol 7, Issue 1, p38
- ISSN
2071-2987
- Publication type
Article