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 |
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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 |
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Country/Territory | France |
City | Paris |
Period | 16/04/18 → 19/04/18 |