• E. Ambika
• K. Mohana

The rapid growth in popularity of economic activities can help to gather a huge amount of economic data. Though such data provides excellent opportunities for economic analysis, its poor quality, high-dimensionality and large-volume pose huge challenges on efficient analysis of economic big data. The traditional techniques offer an unsatisfactory performance while embracing the huge varieties of economic features. As a result, a new method was proposed for efficient analysis of high-dimensional economic big data based on innovative Distributed Feature Selection (DFS). Specifically, this method combines the economic feature selection and econometric model construction to reveal the hidden patterns for economic development. However, it requires the prior knowledge of the number of views during distributed feature selection process. Therefore, in this work, Distributed Supervised Multiview Feature Selection (DSMFS) method is proposed for big data analysis. Initially, data pre-processing is used to prepare the high-quality data. Then, an innovative distributed multiview feature selection is proposed to choose the significant and representative features from multidimensional dataset. In this technique, a group or view consists of homogeneous features, describing a unique data characteristic. Different views represent heterogeneous data characteristics. Consequently, a view can be represented by a few representative features in each view, and the information of heterogeneous views can be well kept by the remaining representative features. Finally, the experimental results demonstrate that the proposed DSMFS method can achieve higher performance for analyzing high-dimensional dataset than the existing DFS method.

"Distributed Supervised Multiview Feature Selection for Efficient Big Data Analysis ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.438-442, June 2019, Available :http://www.jetir.org/papers/JETIR1907K66.pdf

"Distributed Supervised Multiview Feature Selection for Efficient Big Data Analysis ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp438-442, June 2019, Available at : http://www.jetir.org/papers/JETIR1907K66.pdf