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Evaluating Economic Performance with Soft Regression

EasyChair Preprint no. 777

12 pagesDate: February 10, 2019


This study demonstrates effective data mining tool under severe limitations of data availability. We present a soft computing method for evaluating economic performance. To avoid computational explosion, we utilize intervals. This will reduce the number attributes in the dataset. Utilizing intervals allows us to overcome difficult modeling problems such as large quantity of missing data, substantial outliers, etc. Finally, case study of evaluating economic performance of the Soviet led East European bloc is presented. In spite of highly unreliable and inaccurate data provided by the officials of the bloc, the method presented here allows to reach solid and reliable conclusions.

Keyphrases: Cross-national model, Data Mining, Fuzzy Logic, Soft Computing, Soft Regression

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Moti Schneider and Arthur Yosef and Eli Shnaider},
  title = {Evaluating Economic Performance with Soft Regression},
  howpublished = {EasyChair Preprint no. 777},

  year = {EasyChair, 2019}}
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