Machine-based mapping of innovation portfolios

Matthias  de Visser; Shengfa  Miao; Gwenn  Englebienne; Anna Maria Sools; Klaasjan  Visscher

Machine-based mapping of innovation portfolios

Matthias de Visser, Shengfa Miao, Gwenn Englebienne, Anna Maria Sools, Klaasjan Visscher

Psychology, Health & Technology

Research output: Contribution to conference › Paper › peer-review

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
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
Country/Territory	Germany
City	Potsdam
Period	10/09/17 → 12/09/17
Internet address	http://www.continuous-innovation.net/events/conferences/2017.html#0

Cite this

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title = "Machine-based mapping of innovation portfolios",

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. ",

author = "{de Visser}, Matthias and Shengfa Miao and Gwenn Englebienne and Sools, {Anna Maria} and Klaasjan Visscher",

year = "2017",

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note = "18th International CINet Conference 2017 ; Conference date: 10-09-2017 Through 12-09-2017",

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TY - CONF

T1 - Machine-based mapping of innovation portfolios

AU - de Visser, Matthias

AU - Miao, Shengfa

AU - Englebienne, Gwenn

AU - Sools, Anna Maria

AU - Visscher, Klaasjan

N1 - Conference code: 18

PY - 2017/9/10

Y1 - 2017/9/10

N2 - 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.

AB - 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.

M3 - Paper

SP - 667

EP - 671

T2 - 18th International CINet Conference 2017

Y2 - 10 September 2017 through 12 September 2017

ER -

Machine-based mapping of innovation portfolios

Abstract

Conference

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Cite this