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Multiclass Classification Using Bayesian Multivariate Adaptive Regression Splines

Journal Article · · Baynesian Analysis
DOI:https://doi.org/10.1214/25-BA1528· OSTI ID:2570745
 [1];  [1]
  1. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
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We present a new Bayesian model for the problem of multiclass classification. In this model, the probabilities of class membership of a given observation are determined by the mean of a latent Gaussian distribution. The mean functions of this latent distribution consist of combinations of highly flexible basis functions of the inputs: multivariate adaptive regression splines (MARS), first developed for multiple regression. We use reversible jump Markov chain Monte Carlo to make inference on the classification model, including the number of basis functions. We compare the probabilistic classification performance of our proposed approach to existing methods on simulated and benchmark data, and compare uncertainty estimates on simulated data. Our proposed method compares favorably with existing Bayesian and frequentist multiclass classification methods in out-of-sample probabilistic classification, and uncertainty estimation of these probabilistic classifications. We examine the fit of the proposed method to a data set of hurricane storm surge levels near Delaware Bay, US, and conclude that sea level rise is a key contributor to damage delivered by storm surge.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
89233218CNA000001
OSTI ID:
2570745
Report Number(s):
LA-UR--23-26911; 10.1214/25-BA1528
Journal Information:
Baynesian Analysis, Journal Name: Baynesian Analysis; ISSN 1936-0975
Publisher:
International Society for Bayesian AnalysisCopyright Statement
Country of Publication:
United States
Language:
English

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Figures / Tables (10)

Figure 1(p. 16)figure Figure 1
Figure 2(p. 17)figure Figure 2
Table 1(p. 18)table Table 1
Table 2(p. 18)table Table 2
Table 3(p. 19)table Table 3
Table 4(p. 21)table Table 4
Table 5(p. 21)table Table 5
Figure 3(p. 23)figure Figure 3
Figure 4(p. 24)figure Figure 4
Figure 5(p. 25)figure Figure 5

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Related Subjects

97 MATHEMATICS AND COMPUTING
latent variables
nonparametric
probit
reversible jump