Advanced conjoint analysis using feature selection via support vector machines

DOI10.1016/j.ejor.2014.09.051zbMath1339.91077OpenAlexW2086314533MaRDI QIDQ2629697

Ricardo Montoya, Sebastián Maldonado, Richard R. Weber

Publication date: 6 July 2016

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2014.09.051

zbMATH Keywords

support vector machinesfeature selectionconjoint analysisbusiness analytics

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Consumer behavior, demand theory (91B42)

Related Items (6)

Preference estimation under bounded rationality: identification of attribute non-attendance in stated-choice data using a support vector machines approach ⋮ Using hierarchical Bayes draws for improving shares of choice predictions in conjoint simulations: a study based on conjoint choice data ⋮ Feature selection with partition differentiation entropy for large-scale data sets ⋮ A decision analytic approach to predicting quality of life for lung transplant recipients: a hybrid genetic algorithms-based methodology ⋮ Evaluating and selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data ⋮ Learning context-dependent choice functions

Uses Software

• bayesm

Cites Work

• Unnamed Item
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• Combining sources of preference data
• Real world performance of choice-based conjoint models
• Solving multiattribute design problems with analytic hierarchy process and conjoint analysis: an empirical comparison
• Estimation methods for choice-based conjoint analysis of consumer preferences
• Conjoint Optimization: An Exact Branch-and-Bound Algorithm for the Share-of-Choice Problem
• Bayesian Statistics and Marketing
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• 10.1162/153244303322753616

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