A Novel Framework for Constructing Partially Monotone Rule Ensembles

Bartley, Christopher; Liu, Wei; Reynolds, Mark

doi:10.1109/icde.2018.00140

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…We analyze two publicly available classifiers, and describe two experiments. The first experiment evaluates MonoXP for explaining two recently proposed tools, COMET 7 (Sivaraman et al, 2020) and monoboost 8 (Bartley et al, 2018). COMET is run on the Auto-MPG 9 dataset studied in earlier work (Sivaraman et al, 2020), with the choice justified by the time the classifier takes to run.…”

Section: Methodsmentioning

confidence: 99%

“…COMET is run on the Auto-MPG 9 dataset studied in earlier work (Sivaraman et al, 2020), with the choice justified by the time the classifier takes to run. monoboost is run on a monotonic dataset with two classes (as required by the tool) (Bartley et al, 2018). We use a monotonic subset (Pi-maMono) of the Pima dataset 10 .…”

Section: Methodsmentioning

confidence: 99%

“…Monotonicity has been investigated in the context of classification (Cano et al, 2019), including neural networks (Sill, 1997;Magdon-Ismail & Sill, 2008;Bonakdarpour et al, 2018;Sivaraman et al, 2020;Liu et al, 2020), random forests (Bartley et al, 2019) and rule ensembles (Bartley et al, 2018), decision trees (Ben-David et al, 1989;Ben-David, 1995), decision lists (Potharst & Bioch, 2000) and decision rules (Verbeke et al, 2017), support vector machines (Bartley et al, 2016), nearest-neighbor classifiers (Duivesteijn & Feelders, 2008), among others (Fard et al, 2016;Gupta et al, 2016;You et al, 2017;Bonakdarpour et al, 2018). Monotonicity has been studied…”

Section: Introductionmentioning

confidence: 99%

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Explanations for Monotonic Classifiers

Marques-Silva,

Gerspacher,

Cooper

et al. 2021

Preprint

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In many classification tasks there is a requirement of monotonicity. Concretely, if all else remains constant, increasing (resp. decreasing) the value of one or more features must not decrease (resp. increase) the value of the prediction. Despite comprehensive efforts on learning monotonic classifiers, dedicated approaches for explaining monotonic classifiers are scarce and classifier-specific. This paper describes novel algorithms for the computation of one formal explanation of a (black-box) monotonic classifier. These novel algorithms are polynomial in the run time complexity of the classifier and the number of features. Furthermore, the paper presents a practically efficient model-agnostic algorithm for enumerating formal explanations.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Explanations for Monotonic Classifiers

Marques-Silva,

Gerspacher,

Cooper

et al. 2021

Preprint

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show abstract

“…• Evolutionary Hyperrectangle Selection for Monotonic Classification (EHSMC- • MonoBoost ( [50]). Inspired by instance based classifiers, MonoBoost is a framework for monotone additive rule ensembles where partial monotonocity appears.…”

Section: Hybridmentioning

confidence: 99%

Monotonic classification: An overview on algorithms, performance measures and data sets

et al. 2019

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Currently, knowledge discovery in databases is an essential step to identify valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfill restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently,

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RMI-RRG: A Soft Protocol to Postulate Monotonicity Constraints for Tabular Datasets

Vloothuis,

Duivesteijn

2024

Lecture Notes in Computer Science

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A Novel Framework for Constructing Partially Monotone Rule Ensembles

Cited by 4 publications

References 8 publications

Explanations for Monotonic Classifiers

Explanations for Monotonic Classifiers

Monotonic classification: An overview on algorithms, performance measures and data sets

RMI-RRG: A Soft Protocol to Postulate Monotonicity Constraints for Tabular Datasets

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