GMDH2: a web-tool for binary classification via GMDH-type neural network algorithms

Input data

Choose a binary response

Load example data:
Upload a delimited text file:

You can upload your data as separated by comma, tab, semicolon or space.

Note: First row must be header.

Choose type of output

Percentages for qualitative variables (*)
(*) Qualitative variables are summarized with frequency (%).
Decimal places for qualitative variables

Option for quantitative variables (**)
(**) Quantitative variables are summarized with mean +- sd (median, min - max) or mean +- sd (median, Q1 - Q3).
Decimal places for quantitative variables

Choose the algorithm

Specify the selection pressure

Specify the number of maximum layers

Specify the number of maximum neurons

Choose the external criterion


Choose the data

Scatter plot with classification labels


Choose the data

Input new data without response variable (*)
(*) The variables of new data should be in same order.

Load example data:
Upload a delimited text file:

You can upload your data as separated by comma, tab, semicolon or space.

Note: First row must be header.


Group Method of Data Handling (GMDH) - type neural network algorithms are the self organizing machine learning algorithms for the modelling of complex systems. GMDH algorithms are used for different purposes; examples include pattern recognition, classification, clustering, the approximation of multidimensional processes, forecasting, etc.

This web-tool enables the researchers to performs binary classification via GMDH-type neural network algorithm. There exist two main algorithms, GMDH algorithm and diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. GMDH algorithm performs classification for a binary response and returns important variables dominating the system. dce-GMDH algorithm performs binary classification by assembling classifiers based on GMDH algorithm.

This web-tool is a web-interface of the GMDH2 package in R. The tool also produces a well-formatted table of descriptives for a binary response 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.



            

Usage of the web-tool

(i) load your data set and define the binary response variable using Data upload tab.

(ii) obtain descriptive statistics by groups using Describe data tab.

(iii) specify the algorithm and its arguments in Algorithms tab.

(iv) obtain confusion matrix and related statistics for train, validation and test sets in the Results tab. Researchers can also download predicted probabilities and classes (as csv).

(v) draw scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance in the Visualize tab.

(vi) load new data for prediction in the New data tab. Researchers can obtain predicted probabilities and classes in predictions subtab and download them (as csv) with download button.

If there are missing values in the data, a listwise deletion will be applied and a complete-case analysis will be performed. The seed number is fixed to 12345 for reproducibility.

The data are divided into three sets; train (60%), validation (20%) and test (20%) sets. Train set is included in model building. Validation set is used for neuron selection. Test set is utilized to estimate the performance of the methods on unseen data.

Authors

Osman Dag

Department of Biostatistics, Hacettepe University, Ankara, Turkey

osman.dag@hacettepe.edu.tr

Erdem Karabulut

Department of Biostatistics, Hacettepe University, Ankara, Turkey

ekarabul@hacettepe.edu.tr

Reha Alpar

Department of Biostatistics, Hacettepe University, Ankara, Turkey

ralpar@hacettepe.edu.tr


News

Version 1.1 (June 5, 2018)

Minor improvements and fixes.


Version 1.0 (May 23, 2018)

Web-tool version of the GMDH2 package has been released.


Please contact us for any bugs and requests.