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 language | English |
|---|---|
| Title of host publication | Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018 |
| Place of Publication | USA |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 1324-1327 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538655207 |
| DOIs | |
| Publication status | Published - 24 Oct 2018 |
| Event | 34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France Duration: 16 Apr 2018 → 19 Apr 2018 |
Conference
| Conference | 34th IEEE International Conference on Data Engineering, ICDE 2018 |
|---|---|
| Country | France |
| City | Paris |
| Period | 16/04/18 → 19/04/18 |
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A novel framework for constructing partially monotone rule ensembles. / Bartley, Christopher; Liu, Wei; Reynolds, Mark.
Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018. USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 1324-1327 8509361.Research output: Chapter in Book/Conference paper › Conference paper
TY - GEN
T1 - A novel framework for constructing partially monotone rule ensembles
AU - Bartley, Christopher
AU - Liu, Wei
AU - Reynolds, Mark
PY - 2018/10/24
Y1 - 2018/10/24
N2 - 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.
AB - 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.
KW - Classification
KW - Domain Knowledge
KW - Instance-Based
KW - Monotonicity
UR - http://www.scopus.com/inward/record.url?scp=85057079026&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2018.00140
DO - 10.1109/ICDE.2018.00140
M3 - Conference paper
SP - 1324
EP - 1327
BT - Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - USA
ER -