A novel framework for constructing partially monotone rule ensembles

Christopher Bartley, Wei Liu, Mark Reynolds

Research output: Chapter in Book/Conference paperConference paper

1 Citation (Scopus)

Abstract

In many machine learning applications there exists prior knowledge that the response variable should be non-decreasing in one or more of the features. For example, the chance of a tumour being malignant should not decrease with increasing diameter (all else being equal). While a number of classification algorithms make use of monotone knowledge, many are limited to full monotonicity (in all features). Taking inspiration from instance based classifiers, we present a framework for monotone additive rule ensembles that is the first to cater for partial monotonicity (in some features). We demonstrate it by developing a partially monotone instance based classifier based on L1 cones. Experiments show that the algorithm produces reasonable results on real data sets while ensuring perfect partial monotonicity.

Original languageEnglish
Title of host publicationProceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1324-1327
Number of pages4
ISBN (Electronic)9781538655207
DOIs
Publication statusPublished - 24 Oct 2018
Event34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France
Duration: 16 Apr 201819 Apr 2018

Conference

Conference34th IEEE International Conference on Data Engineering, ICDE 2018
CountryFrance
CityParis
Period16/04/1819/04/18

Fingerprint Dive into the research topics of 'A novel framework for constructing partially monotone rule ensembles'. Together they form a unique fingerprint.

Cite this