Distributed Feature Selection for Efficient Economic Big Data Analysis - 2018


With the rapidly increasing popularity of economic activities, a large amount of economic data is being collected. Although such knowledge offers super opportunities for economic analysis, its low-quality, high-dimensionality and huge-volume cause nice challenges on efficient analysis of economic massive knowledge. The existing methods have primarily analyzed economic knowledge from the attitude of econometrics, that involves restricted indicators and demands prior information of economists. When embracing large sorts of economic factors, these ways tend to yield unsatisfactory performance. To address the challenges, this Project presents a brand new framework for efficient analysis of high-dimensional economic massive knowledge primarily based on innovative distributed feature choice. Specifically, the framework combines the methods of economic feature choice and econometric model construction to reveal the hidden patterns for economic development. The functionality rests on three pillars: (i) novel knowledge pre-processing techniques to prepare high-quality economic data, (ii) an innovative distributed feature identification solution to find important and representative economic indicators from multidimensional data sets, and (iii) new econometric models to capture the hidden patterns for economic development. The experimental results on the economic information collected in Dalian, China, demonstrate that our proposed framework and ways have superior performance in analyzing monumental economic information.

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