High accuracy partially monotone ordinal classication

Christopher Bartley

Research output: Thesis › Doctoral Thesis

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Abstract

This thesis aimed to answer the question: can domain knowledge regarding nondecreasing (monotone) relationships be incorporated into high performance classification algorithms without compromising their performance? Two high accuracy classifiers (Support Vector Machines and Random Forest) and a foundational approach (instance based partial orders) were extended into novel versions that demonstrated improvements in one or more of the following: global monotonicity (guaranteeing compliance in all circumstances), partial monotonicity (allowing non-monotone features), prediction accuracy, and scalability. It is hoped this thesis encourages and enables practitioners to include knowledge of monotonicity in their models, for the sake of accuracy, simplicity, and interpretability.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
Thesis sponsors
Award date13 Jun 2019
DOIs
Publication statusUnpublished - 2018

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Support vector machines
Scalability
Classifiers
Compliance

Cite this

Bartley, Christopher. / High accuracy partially monotone ordinal classication. 2018.
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abstract = "This thesis aimed to answer the question: can domain knowledge regarding nondecreasing (monotone) relationships be incorporated into high performance classification algorithms without compromising their performance? Two high accuracy classifiers (Support Vector Machines and Random Forest) and a foundational approach (instance based partial orders) were extended into novel versions that demonstrated improvements in one or more of the following: global monotonicity (guaranteeing compliance in all circumstances), partial monotonicity (allowing non-monotone features), prediction accuracy, and scalability. It is hoped this thesis encourages and enables practitioners to include knowledge of monotonicity in their models, for the sake of accuracy, simplicity, and interpretability.",
keywords = "Machine learning, Monotonicity, Domain knowledge, Classification",
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Bartley, C 2018, 'High accuracy partially monotone ordinal classication', Doctor of Philosophy, The University of Western Australia. https://doi.org/10.26182/5d19aff34917e

High accuracy partially monotone ordinal classication. / Bartley, Christopher.

2018.

Research output: Thesis › Doctoral Thesis

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AB - This thesis aimed to answer the question: can domain knowledge regarding nondecreasing (monotone) relationships be incorporated into high performance classification algorithms without compromising their performance? Two high accuracy classifiers (Support Vector Machines and Random Forest) and a foundational approach (instance based partial orders) were extended into novel versions that demonstrated improvements in one or more of the following: global monotonicity (guaranteeing compliance in all circumstances), partial monotonicity (allowing non-monotone features), prediction accuracy, and scalability. It is hoped this thesis encourages and enables practitioners to include knowledge of monotonicity in their models, for the sake of accuracy, simplicity, and interpretability.

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Bartley C. High accuracy partially monotone ordinal classication. 2018. https://doi.org/10.26182/5d19aff34917e

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  • THESIS - DOCTOR OF PHILOSOPHY - BARTLEY Christopher Wayne - 2018

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