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Random Forests for multiclass classification: Random MultiNomial Logit
6 references·10 citing·52 second hop
Random Forests for multiclass classification: Random MultiNomial Logit (2007)Random Forests for multiclass classificat…Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting (2004)Customer base analysis: parti…Deriving Categories to Achieve Goals (1991)Deriving Categories to Achiev…Discrete choice analysis: Theory and application to travel demand (1988)Discrete choice analysis: The…Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Discrete Choice Analysis: The…Consumer Choice Strategies for Comparing Noncomparable Alternatives (1984)Consumer Choice Strategies fo…Discrete choice theory, information theory and the multinomial logit and gravity models (1983)Discrete choice theory, infor…A comparative study of automated legal text classification using random forests and deep learning (2021)A comparative study of automa…Integrated Long-Term Stock Selection Models Based on Feature Selection and Machine Learning Algorithms for China Stock Market (2020)Integrated Long-Term Stock Se…Detecting corporate misconduct through random forest in China’s construction industry (2020)Detecting corporate misconduc…Online big data-driven oil consumption forecasting with Google trends (2018)Online big data-driven oil co…Predicting the listing statuses of Chinese-listed companies using decision trees combined with an improved filter feature selection method (2017)Predicting the listing status…Credit scoring by feature-weighted support vector machines (2013)Credit scoring by feature-wei…Customer event history for churn prediction: How long is long enough? 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References

  • 1987Discrete Choice Analysis: Theory and Application to Travel Demand.5,542 cited
  • 1988Discrete choice analysis: Theory and application to travel demand3,257 cited
  • 1991Deriving Categories to Achieve Goals556 cited
  • 2004Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting425 cited
  • 1983Discrete choice theory, information theory and the multinomial logit and gravity models367 cited
  • 1984Consumer Choice Strategies for Comparing Noncomparable Alternatives349 cited

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  • 2011Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics264 cited
  • 2021A comparative study of automated legal text classification using random forests and deep learning242 cited
  • 2018Online big data-driven oil consumption forecasting with Google trends236 cited
  • 2020Integrated Long-Term Stock Selection Models Based on Feature Selection and Machine Learning Algorithms for China Stock Market158 cited
  • 2011An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction132 cited
  • 2012Customer event history for churn prediction: How long is long enough?77 cited
  • 2020Detecting corporate misconduct through random forest in China’s construction industry56 cited

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2017
Predicting the listing statuses of Chinese-listed companies using decision trees combined with an improved filter feature selection method
50 cited
  • 2009Data augmentation by predicting spending pleasure using commercially available external data38 cited
  • 2013Credit scoring by feature-weighted support vector machines30 cited