DOI: 10.5281/zenodo.2537998
title: Recognising innovative companies by using a diversified stacked generalisation method for website classification – the raw results

description: <strong>Introduction</strong>

The classification models were trained out by using the Classification and Regression Training package (caret) [1]. The models' parameters were fine-tuned by the 10-fold cross-validation procedure [2].

<strong>Cluster parameters</strong>

Most computations were carried out on a cluster having the following parameters:


	GPU: NVIDIA Tesla P100;
	CPU: 2.0 GHz Intel® Xeon® Platinum 8167M;
	The number of GPUs: 2;
	The number of CPU cores: 28;
	The number of CPU threads: 56;
	RAM: 192 GB;
	Storage: 3 TB.


Only one model (k-nn) was calculated on a cluster having the following parameters:


	Processor: Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz 3.40 GHz;
	RAM: 16 GB;
	Windows 64 bit.


<strong>Performance statistics</strong>

All performance statistics are stored in cvs files. Each file corresponds to a particular machine learning method such as a file, "methodName-stat.csv" contains all data regarding a method, "methodName." All files cover the following columns:


	<em>dataSetName – </em>a name of a data set on which evaluation was carried out; there are three possible values: (i) <em>firstPages</em> refers to the first data set (<em>L<sub>D</sub></em>) that contains textual description of a company; (ii)  <em>firstPageLabels</em> refers to the second data set (<em>L<sub>L</sub></em>) that involves link labels that were extracted from an index page; (iii) <em>aggregateDocument</em> refers to the third data set (<em>L<sub>B</sub></em>) that consists of a so-called big document;
	<em>fmeasure</em> - the number of features that were taken into account during  evaluation;
	<em>method</em>  - the name of function in the caret package;
	<em>parameters</em> - the values of parameters received from a tuning phase of a given classification method;
	<em>precision </em>– the value of method’s precision;
	<em>recall </em>– the value of method’s recall;
	<em>fmeasure</em>  - the value of method’s F-measure; 
	<em>error</em> - the value of method’s error;
	<em>acc </em>– the value of method’s.


<strong>Time processing statistics</strong>

All time processing statistics, like the performance statistics, are stored in cvs files. Each file corresponds to a particular machine learning method such as a file, "methodName-time.csv". All files cover the following columns:


	<em>dataSetName – </em>a name of a data set on which evaluation was carried out; there are three possible values: (i) <em>firstPages</em> refers to the first data set (<em>L<sub>D</sub></em>) that contains textual description of a company; (ii)  <em>firstPageLabels</em> refers to the second data set (<em>L<sub>L</sub></em>) that involves link labels that were extracted from an index page; (iii) <em>aggregateDocument</em> refers to the third data set (<em>L<sub>B</sub></em>) that consists of a so-called big document;
	<em>featureNo</em> - the number of features that were taken into account during  evaluation;
	<em>method</em>  - the name of function in the caret package;
	<em>user</em> - user time elapsed for executing a <em>method</em> as an R process;
	<em>system</em>  - system time elapsed for executing a <em>method</em> as an R process;
	<em>elapsed</em> - total time elapsed for executing a <em>method</em> as an R process.


For more information about user, system and total elapsed time, please see documentation [3].

<strong>References</strong>

[1] https://cran.r-project.org/web/packages/caret/

[2] https://topepo.github.io/caret/model-training-and-tuning.html

[3] https://stat.ethz.ch/R-manual/R-devel/library/base/html/proc.time.htm