Machine learning techniques show a great promise for improving innovation portfolio management. In this paper we experiment with different methods to classify innovation projects of a high-tech firm as either explorative or exploitative, and compare the results with a manual, theory-based mapping of these projects and with expert classification. We find that by combining a high-information extraction method with a decision tree or maximum entropy algorithm, higher levels of accuracy can be reached. Opportunities and limitations of different methods are discussed.
|Number of pages||4|
|Publication status||Published - 10 Sep 2017|
|Event||18th International CINet Conference - Potsdam, Germany|
Duration: 10 Sep 2017 → 12 Sep 2017
Conference number: 18
|Conference||18th International CINet Conference|
|Period||10/09/17 → 12/09/17|