Interval Type-2 Fuzzy weighted Extreme Learning Machine for GDP Prediction
Amit K. Shukla, Sandeep Kumar, Rishi Jagdev, Pranab Kumar Muhuri, Q. M. Danish Lohani · 2018
The CO2emission due to industrialization is a crucial parameter which is directly proportional to the economic growth of any country/nation. However, for the non-accessible nations and war-torn nations with highly unreliable or insufficient macroeconomic data, the prediction of gross domestic product (GDP) is a challenging task. Thus, in this paper, we have proposed a novel approach for the reliable GDP estimation utilizing only the CO2emission data. For this purpose, transfer learning (TL) is applied which learns on the previously acquired information and solve the new task. The training is performed on the GDP data of the developed nations and then prediction is estimated for the developing nations. This is implemented using kernel extreme learning machine (KELM) in which the output weights are modelled using interval type-2 fuzzy sets (IT2 Fss) for the effective transferal of knowledge from developed to developing nation. Experimental results have shown that the proposed IT2F-KELM provide much-improved RMSE as compared with the traditional KELM.