Using wing morphology to separate four species of<i>Acrocephalus</i>warblers in Scandinavia

Malmhagen, Björn; Karlsson, Måns; Menzie, Stephen

doi:10.1080/03078698.2013.811190

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…To a large extent, our data is the same as in Malmhagen et al (2013), where the same classification problem is treated with mostly descriptive statistics. The four species considered are Reed warbler, Marsh warbler, Blyth's reed warbler and Paddyfield warbler, and the three shared traits are wing length, notch length and notch position.…”

Section: Illustration For An Ornithological Data Setmentioning

confidence: 99%

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Classification Under Partial Reject Options

Karlsson¹,

Hössjer²

2022

Preprint

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We study set-valued classification for a Bayesian model where data originates from one of a finite number N of possible hypotheses. Thus we consider the scenario where the size of the classified set of categories ranges from 0 to N . Empty sets corresponds to an outlier, size 1 represents a firm decision that singles out one hypotheses, size N corresponds to a rejection to classify, whereas sizes 2 . . . , N − 1 represent a partial rejection, where some hypotheses are excluded from further analysis. We introduce a general framework of reward functions with a set-valued argument and derive the corresponding optimal Bayes classifiers, for a homogeneous block of hypotheses and for when hypotheses are partitioned into blocks, where ambiguity within and between blocks are of different severity. We illustrate classification using an ornithological dataset, with taxa partitioned into blocks and parameters estimated using MCMC. The associated reward function's tuning parameters are chosen through cross-validation.

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Section: Illustration For An Ornithological Data Setmentioning

confidence: 99%

“…For details on the measurements of the traits, see e.g. Svensson (1992); Malmhagen et al (2013) and Karlsson and Hössjer (2021). In total we have 882 complete observations of juvenile birds, with the number n i of birds of each species given in Table 1.…”

Section: Illustration For An Ornithological Data Setmentioning

confidence: 99%

Classification Under Partial Reject Options

Karlsson¹,

Hössjer²

2022

Preprint

Self Cite

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Classification Under Partial Reject Options

Karlsson,

Hössjer

2023

J Classif

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In many applications there is ambiguity about which (if any) of a finite number N of hypotheses that best fits an observation. It is of interest then to possibly output a whole set of categories, that is, a scenario where the size of the classified set of categories ranges from 0 to N. Empty sets correspond to an outlier, sets of size 1 represent a firm decision that singles out one hypothesis, sets of size N correspond to a rejection to classify, whereas sets of sizes $$2,\ldots ,N-1$$ 2 , … , N - 1 represent a partial rejection to classify, where some hypotheses are excluded from further analysis. In this paper, we review and unify several proposed methods of Bayesian set-valued classification, where the objective is to find the optimal Bayesian classifier that maximizes the expected reward. We study a large class of reward functions with rewards for sets that include the true category, whereas additive or multiplicative penalties are incurred for sets depending on their size. For models with one homogeneous block of hypotheses, we provide general expressions for the accompanying Bayesian classifier, several of which extend previous results in the literature. Then, we derive novel results for the more general setting when hypotheses are partitioned into blocks, where ambiguity within and between blocks are of different severity. We also discuss how well-known methods of classification, such as conformal prediction, indifference zones, and hierarchical classification, fit into our framework. Finally, set-valued classification is illustrated using an ornithological data set, with taxa partitioned into blocks and parameters estimated using MCMC. The associated reward function’s tuning parameters are chosen through cross-validation.

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Identification of taxon through classification with partial reject options

Karlsson

Hössjer

2023

Journal of the Royal Statistical Society Series C: Applied Statistics

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Identification of taxa can significantly be assisted by statistical classification based on trait measurements either individually or by phylogenetic (clustering) methods. In this article, we present a general Bayesian approach for classifying species individually based on measurements of a mixture of continuous and ordinal traits, and any type of covariates. The trait vector is derived from a latent variable with a multivariate Gaussian distribution. Decision rules based on supervised learning are presented that estimate model parameters through blocked Gibbs sampling. These decision regions allow for uncertainty (partial rejection), so that not necessarily one specific category (taxon) is output when new subjects are classified, but rather a set of categories including the most probable taxa. This type of discriminant analysis employs reward functions with a set-valued input argument, so that an optimal Bayes classifier can be defined. We also present a way of safeguarding against outlying new observations, using an analogue of a p-value within our Bayesian setting. We refer to our Bayesian set-valued classifier as the Karlsson–Hössjer method, and it is illustrated on an original ornithological data set of birds. We also incorporate model selection through cross-validation, exemplified on another original data set of birds.

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Using wing morphology to separate four species ofAcrocephaluswarblers in Scandinavia

Cited by 4 publications

References 3 publications

Classification Under Partial Reject Options

Classification Under Partial Reject Options

Classification Under Partial Reject Options

Identification of taxon through classification with partial reject options

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