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Predicting the Innovation Activity of Chemical Firms Using an Ensemble of Decision Trees
Year: 2015
Type of publication: článek ve sborníku
Name of source: Proceedings from 11th International Conference on Innovations in Information Technology
Publisher name: IEEE (Institute of Electrical and Electronics Engineers)
Place: New York
Page from-to: 35-39
Titles:
Language Name Abstract Keywords
cze Predikování inovační aktivity chemických firem použitím rozhodovacích stromů Velké množství studií se zabývá analýzou a predikováním inovační aktivity. Je tomu tak proto, že inovační aktivita firem je jedním z fundamentálních determinantů jejich konkurenceschopnosti. Mnoho studií používá lineární (logistickou) regresi. Jenže tato nebere v úvahu všechny rekurzivní faktory týkající se inovační činnosti. V příspěvku demonstrujeme použití souboru rozhodovacích stromů, kterými modelujeme nelineární charakteristiky inovačních procesů u vybraného souboru chemicky orientovaných firem. inovační aktivita;predikční modely;rozhodovacích stromy;chemické firmy
eng Predicting the Innovation Activity of Chemical Firms Using an Ensemble of Decision Trees A number of studies are concerned with the analysis of predicting innovation activity, because companies' innovation activity is one of the fundamental determinants for their competitiveness. However, most studies use a linear (logistic) regression model for their analysis. This, however, is not able to take into account all the recursive terms concerning a company's innovation activity. Therefore, in the report we demonstrate the use of ensembles of decision trees to model the intrinsic nonlinear characteristics of the innovation process. We apply this method for predicting innovation activity to chemical companies. We show that internal knowledge spillovers were the most important determinant for the chemical firms' innovation activity during the monitored period. Furthermore, R&D intensity, collaboration on innovation and firm size were also important determinants. innovation activity;predicting models;decision trees;chemical firms

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