Recognising innovative companies by using a diversified stacked generalisation method for website classification – the raw results

Introduction 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]. Cluster parameters 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. Performance statistics 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: dataSetName – a name of a data set on which evaluation was carried out; there are three possible values: (i) firstPages refers to the first data set (LD) that contains textual description of a company; (ii) firstPageLabels refers to the second data set (LL) that involves link labels that were extracted from an index page; (iii) aggregateDocument refers to the third data set (LB) that consists of a so-called big document; fmeasure - the number of features that were taken into account during evaluation; method - the name of function in the caret package; parameters - the values of parameters received from a tuning phase of a given classification method; precision – the value of method’s precision; recall – the value of method’s recall; fmeasure - the value of method’s F-measure; error - the value of method’s error; acc – the value of method’s. Time processing statistics 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: dataSetName – a name of a data set on which evaluation was carried out; there are three possible values: (i) firstPages refers to the first data set (LD) that contains textual description of a company; (ii) firstPageLabels refers to the second data set (LL) that involves link labels that were extracted from an index page; (iii) aggregateDocument refers to the third data set (LB) that consists of a so-called big document; featureNo - the number of features that were taken into account during evaluation; method - the name of function in the caret package; user - user time elapsed for executing a method as an R process; system - system time elapsed for executing a method as an R process; elapsed - total time elapsed for executing a method as an R process. For more information about user, system and total elapsed time, please see documentation [3]. References [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