Reclassification of Countries According to Human Development Index: An Application With Ann and Anfis Methods

Abstract

Classification problems are frequently encountered in the fields of statistics, econometrics and data mining. Techniques used to solve the problem are changing and developing day by day depending on the technology of the age. For this purpose, besides multivariate statistical techniques, methods based on fuzzy and artificial intelligence are also used today. This study aims to make a comparison between the classification performances of artificial neural network (ANN) from machine learning techniques and Adaptive Neural Fuzzy Inference System (ANFIS), which is a combination of ANN and fuzzy logic technique and is based on hybrid learning technique. For this purpose, the countries were classified according to the Human Development Index (HDI) and ANN and ANFIS methods and the results were compared with the HDI. In this context, the HDI of 2015 was measured for 185 countries by using 27 development indicators under eight main topics of health, entrepreneurship, macroeconomics and microeconomics, logistics, trade, social life and natural factors and classification of these countries was estimated. When the analysis results are considered, in economic terms, development is composed of seven factors and eight main subjects according to the estimated index calculated in the study, which is different from the HDI. In terms of statistics, countries have been classified correctly at a rate of 87.5% according to ANN and 91.36% according to ANFIS. In this case, it was observed that the ANFIS method gave better results than ANN.

Sensor fault detection and reconstruction system for commercial aircrafts

“…Output membership functions are all the same type of constant or linear function. The items that need to be determined in order to use the application are the number of linguistic variables, membership function type, degree of fuzzy inference, optimisation method, error tolerance and epoch number [38]. Three membership functions are defined for each input to train ANFIS.…”

“…In order to use Tan-Sigmoid Transfer Function, input values are first normalised between (−1, 1) and consequently output values are obtained between (−1, 1). Tan-Sigmoid Transfer Function is given in Equation ( 9) [38]. The hidden layer number is taken as 1 for the algorithm to work faster.…”

“…The total input output real data set which is collected from a commercial aircraft is (36,550) and the total input output dataset is randomly divided into training (25,585) and test set (10,965). The net output of the neural network is calculated using Equation ( 10) [38].…”

“…Machine learning methods are widely used in recent studies for modeling and estimation especially in the aerospace fields [3][4][5][6][7][8][9]. In this study, machine learning based methods which are Adaptive Neural Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) were used in the estimation phase [38]. MATLAB software was used for all the calculations.…”