Classification of functional fragments by regularized linear
classifiers with domain selection

J 2019

Classification of functional fragments by regularized linear classifiers with domain selection

KRAUS, David a Marco STEFANUCCI

Základní údaje

Originální název

Classification of functional fragments by regularized linear classifiers with domain selection

Autoři

KRAUS, David a Marco STEFANUCCI

Vydání

Biometrika, Oxford, Oxford Univ Press, 2019, 0006-3444

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10103 Statistics and probability

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Full Text

Impakt faktor

Impact factor: 1.632

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/19:00107190

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

Classification; Conjugate gradients; Domain selection; Functional data; Partial observation; Regularization; Ridge method

Štítky

H20, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 10. 3. 2020 13:27, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

We consider classification of functional data into two groups by linear classifiers based on one-dimensional projections of functions. We reformulate the task of finding the best classifier as an optimization problem and solve it by the conjugate gradient method with early stopping, the principal component method, and the ridge method. We study the empirical version with finite training samples consisting of incomplete functions observed on different subsets of the domain and show that the optimal, possibly zero, misclassification probability can be achieved in the limit along a possibly nonconvergent empirical regularization path. We propose a domain extension and selection procedure that finds the best domain beyond the common observation domain of all curves. In a simulation study we compare the different regularization methods and investigate the performance of domain selection. Our method is illustrated on a medical dataset, where we observe a substantial improvement of classification accuracy due to domain extension.

Návaznosti

GJ17-22950Y, projekt VaV

Název: Statistická inference pro složité náhodné procesy v ekonometrickém modelování

Investor: Grantová agentura ČR, Statistická inference pro složité náhodné procesy v ekonometrickém modelování

Citovat

KRAUS, David a Marco STEFANUCCI. Classification of functional fragments by regularized linear classifiers with domain selection. Biometrika. Oxford: Oxford Univ Press, 2019, roč. 106, č. 1, s. 161-180. ISSN 0006-3444. Dostupné z: https://doi.org/10.1093/biomet/asy060.

@article{1468976,
   author = {Kraus, David and Stefanucci, Marco},
   article_location = {Oxford},
   article_number = {1},
   doi = {https://doi.org/10.1093/biomet/asy060},
   keywords = {Classification; Conjugate gradients; Domain selection; Functional data; Partial observation; Regularization; Ridge method},
   language = {eng},
   issn = {0006-3444},
   journal = {Biometrika},
   title = {Classification of functional fragments by regularized linear classifiers with domain selection},
   url = {https://academic.oup.com//biomet/advance-article/doi/10.1093/biomet/asy060/5250873?guestAccessKey=64dbd3b6-5ffc-4dee-8586-3fee35ebded7},
   volume = {106},
   year = {2019}
}

TY  - JOUR
ID  - 1468976
AU  - Kraus, David - Stefanucci, Marco
PY  - 2019
TI  - Classification of functional fragments by regularized linear classifiers with domain selection
JF  - Biometrika
VL  - 106
IS  - 1
SP  - 161-180
EP  - 161-180
PB  - Oxford Univ Press
SN  - 00063444
KW  - Classification
KW  - Conjugate gradients
KW  - Domain selection
KW  - Functional data
KW  - Partial observation
KW  - Regularization
KW  - Ridge method
UR  - https://academic.oup.com//biomet/advance-article/doi/10.1093/biomet/asy060/5250873?guestAccessKey=64dbd3b6-5ffc-4dee-8586-3fee35ebded7
L2  - https://academic.oup.com//biomet/advance-article/doi/10.1093/biomet/asy060/5250873?guestAccessKey=64dbd3b6-5ffc-4dee-8586-3fee35ebded7
N2  - We consider classification of functional data into two groups by linear classifiers based on one-dimensional projections of functions. We reformulate the task of finding the best classifier as an optimization problem and solve it by the conjugate gradient method with early stopping, the principal component method, and the ridge method. We study the empirical version with finite training samples consisting of incomplete functions observed on different subsets of the domain and show that the optimal, possibly zero, misclassification probability can be achieved in the limit along a possibly nonconvergent empirical regularization path. We propose a domain extension and selection procedure that finds the best domain beyond the common observation domain of all curves. In a simulation study we compare the different regularization methods and investigate the performance of domain selection. Our method is illustrated on a medical dataset, where we observe a substantial improvement of classification accuracy due to domain extension.
ER  -

KRAUS, David a Marco STEFANUCCI. Classification of functional fragments by regularized linear classifiers with domain selection. \textit{Biometrika}. Oxford: Oxford Univ Press, 2019, roč.~106, č.~1, s.~161-180. ISSN~0006-3444. Dostupné z: https://doi.org/10.1093/biomet/asy060.

Přehled o publikaci