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optimal pruned K-nearest neighbors: op-knn application to financial modeling

Author

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  • Eric Severin

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, SAMOS - Statistique Appliquée et MOdélisation Stochastique - UP1 - Université Paris 1 Panthéon-Sorbonne, CIS - Laboratory of Computer and Information Science - TKK - TKK Helsinki University of Technology)

  • Yoan Miche

    (CIS - Laboratory of Computer and Information Science - TKK - TKK Helsinki University of Technology)

  • Amaury Lendasse

    (CIS - Laboratory of Computer and Information Science - TKK - TKK Helsinki University of Technology, DICE-MLG - Machine Learning Group - UCL - Université Catholique de Louvain = Catholic University of Louvain)

  • Anti Sorjamaa

    (TKK - TKK Helsinki University of Technology)

  • Qi Yu

    (TKK - TKK Helsinki University of Technology)

Abstract

The paper proposes a methodology called OP-KNN, which builds a one hidden- layer feedforward neural network, using nearest neighbors neurons with extremely small com- putational time. The main strategy is to select the most relevant variables beforehand, then to build the model using KNN kernels. Multiresponse Sparse Regression (MRSR) is used as the second step in order to rank each kth nearest neighbor and finally as a third step Leave-One- Out estimation is used to select the number of neighbors and to estimate the generalization performances. This new methodology is tested on a toy example and is applied to financial modeling

Suggested Citation

  • Eric Severin & Yoan Miche & Amaury Lendasse & Anti Sorjamaa & Qi Yu, 2008. "optimal pruned K-nearest neighbors: op-knn application to financial modeling," Working Papers hal-00286065, HAL.
    • Handle: RePEc:hal:wpaper:hal-00286065
    Note: View the original document on HAL open archive server: https://hal.science/hal-00286065
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