European Business Schools Librarian's Group

Les Cahiers de Recherche,
HEC Paris

No 1097: Classification Models Via Tabu Search: An Application to Early Stage Venture Classification

Thomas Astebro , Canan Akdemir and Samir Elhedhli
Additional contact information
Thomas Astebro: HEC Paris, Postal: 1 rue de la Libération; 78350 Jouy-en-Josas
Canan Akdemir: University of Waterloo, Postal: 200 University Ave W, Waterloo, ON N2L 3G1, Canada
Samir Elhedhli: University of Waterloo, Postal: 200 University Ave W, Waterloo, ON N2L 3G1, Canada

Abstract: We model the decision making process used by experts at the Canadian Innovation Centre to classify early stage venture proposals based on potential commercial success. The decision is based on thirty seven attributes that take values in (-1; 0; 1). We adopt a conjunctive decision framework due to Astebro and Elhedhli that selects a subset of attributes and determines two threshold values: one for the maximum allowed negatives (n) and one for minimum required positives (p). A proposal is classified as a success if the number of positives is greater than or equal to p and the number of negatives is less than or equal to n over the selected attributes. Based on data of 561 observations, the selection of attributes and the determination of the threshold values is modeled as a large-scale mixed integer program. Two solution approaches are explored: Benders decomposition and Tabu search. The first was very slow to converge, while the second provided high quality solutions quickly. Tabu Search provides excellent classification accuracy for predicting commercial successes as well as replicating the experts' forecasts, opening the venue for the use of Tabu Search in scoring and classification problems.

Keywords: tabu search; large-scale mixed integer program; classification; decision heuristic; early stage venture forecast

JEL-codes: C45; C53; C63

21 pages, October 21, 2015

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