A model-free soft classification with a functional predictor

Eugene Lee, Seung Jun Shin

Research output: Contribution to journalArticlepeer-review


Class probability is a fundamental target in classification that contains complete classification information. In this article, we propose a class probability estimation method when the predictor is functional. Motivated by Wang et al. (Biometrika, 95, 149-167, 2007), our estimator is obtained by training a sequence of functional weighted support vector machines (FWSVM) with different weights, which can be justified by the Fisher consistency of the hinge loss. The proposed method can be extended to multiclass classification via pairwise coupling proposed by Wu et al. (Journal of Machine Learning Research, 5, 975-1005, 2004). The use of FWSVM makes our method model-free as well as computationally efficient due to the piecewise linearity of the FWSVM solutions as functions of the weight. Numerical investigation to both synthetic and real data show the advantageous performance of the proposed method.

Original languageEnglish
Pages (from-to)635-644
Number of pages10
JournalCommunications for Statistical Applications and Methods
Issue number6
Publication statusPublished - 2019

Bibliographical note

Funding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MIST), Grant number 2018R1D1A1B07043034 and No.2019R1A4A1028134.

Publisher Copyright:
© Korean Statistical Society.


  • Fisher consistency
  • Functional data
  • Probability estimation
  • Support vector machines

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Finance
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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