Abstract
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.
| Original language | English |
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| Pages | 667-671 |
| Number of pages | 4 |
| Publication status | Published - 10 Sept 2017 |
| Event | 18th International CINet Conference 2017 - Potsdam, Germany Duration: 10 Sept 2017 → 12 Sept 2017 Conference number: 18 http://www.continuous-innovation.net/events/conferences/2017.html#0 |
Conference
| Conference | 18th International CINet Conference 2017 |
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| Country/Territory | Germany |
| City | Potsdam |
| Period | 10/09/17 → 12/09/17 |
| Internet address |