GMDH2: Binary Classification via GMDH-Type Neural Network Algorithms-R Package and Web-Based Tool

Download
2019-01-01
DAĞ, OSMAN
KARABULUT, ERDEM
Alpar, Reha
Group method of data handling (GMDH)-type neural network algorithms are the self-organizing algorithms for modeling complex systems. GMDH algorithms are used for different objectives; examples include regression, classification, clustering, forecasting, and so on. In this paper, we present GMDH2 package to perform binary classification via GMDH-type neural network algorithms. The package offers two main algorithms: GMDH algorithm and diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. GMDH algorithm performs binary classification and returns important variables. dce-GMDH algorithm performs binary classification by assembling classifiers based on GMDH algorithm. The package also provides a well-formatted table of descriptives in different format (R, LaTeX, HTML). Moreover, it produces confusion matrix and related statistics, and scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance. Moreover, a user-friendly web-interface of the package is provided especially for non-R users. (c) 2019 The Authors. Published by Atlantis Press SARL.
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS

Suggestions

GMDH: An R Package for Short Term Forecasting via GMDH-Type Neural Network Algorithms
DAĞ, OSMAN; Yozgatlıgil, Ceylan (2016-08-01)
Group Method of Data Handling (GMDH)-type neural network algorithms are the heuristic self organization method for the modelling of complex systems. GMDH algorithms are utilized for a variety of purposes, examples include identification of physical laws, the extrapolation of physical fields, pattern recognition, clustering, the approximation of multidimensional processes, forecasting without models, etc. In this study, the R package GMDH is presented to make short term forecasting through GMDH-type neural n...
GMDH-type neural network algorithms for short term forecasting
Dağ, Osman; Yozgatlıgil, Ceylan; Department of Statistics (2015)
Group Method of Data Handling (GMDH) - type neural network algorithms are the heuristic self-organization method for modelling the complex systems. GMDH algorithms are utilized for the variety of purposes, which are identification of physical laws, extrapolation of physical fields, pattern recognition, clustering, approximation of multidimensional processes, forecasting without models and so on. In this study, GMDH - type neural network algorithms were applied to make forecasts for time series data sets. We...
girdap: Open source object-oriented autonomous grid management library for solving equations of conservation laws
Uzgoren, Eray (Elsevier BV, 2017-10-12)
girdap is an object-oriented grid generation and management library that uses finite volume operator objects to provide researchers and educators a framework to solve different sets of algebraic and differential equations on multiple grid objects, which are allowed to interact with each other. Grid objects have the capability of performing local anisotropic grid refinement (h-adaptation) as well as relocating their vertices (r-adaptation) to resolve length scales based on solution field obtained using algeb...
Optimum design of grillage systems using harmony search algorithm
Erdal, Ferhat; Saka, Mehmet Polat; Department of Engineering Sciences (2007)
Harmony search method based optimum design algorithm is presented for the grillage systems. This numerical optimization technique imitates the musical performance process that takes place when a musician searches for a better state of harmony. For instance, jazz improvisation seeks to find musically pleasing harmony similar to the optimum design process which seeks to find the optimum solution. The design algorithm considers the displacement and strength constraints which are implemented from LRFD-AISC (Loa...
Estimation in bivariate nonnormal distributions with stochastic variance functions
Tiku, Moti L.; İslam, Muhammed Qamarul; SAZAK, HAKAN SAVAŞ (Elsevier BV, 2008-01-01)
Data sets in numerous areas of application can be modelled by symmetric bivariate nonnormal distributions. Estimation of parameters in such situations is considered when the mean and variance of one variable is a linear and a positive function of the other variable. This is typically true of bivariate t distribution. The resulting estimators are found to be remarkably efficient. Hypothesis testing procedures are developed and shown to be robust and powerful. Real life examples are given.
Citation Formats
O. DAĞ, E. KARABULUT, and R. Alpar, “GMDH2: Binary Classification via GMDH-Type Neural Network Algorithms-R Package and Web-Based Tool,” INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, pp. 649–660, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66446.