MoNGEL: monotonic nested generalized exemplar learning

Author
Abstract

In supervised prediction problems, the response attribute depends on certain explanatory attributes. Some real problems require the response attribute to represent ordinal values that should increase with some of the explaining attributes. They are called classification problems with monotonicity constraints. In this paper, we aim at formalizing the approach to nested generalized exemplar learning with monotonicity constraints, proposing the monotonic nested generalized exemplar learning (MoNGEL) method. It accomplishes learning by storing objects in $$ \mathbbR^n $$, hybridizing instance-based learning and rule learning into a combined model. An experimental analysis is carried out over a wide range of monotonic data sets. The results obtained have been verified by non-parametric statistical tests and show that MoNGEL outperforms well-known techniques for monotonic classification, such as ordinal learning model, ordinal stochastic dominance learner and k-nearest neighbor, considering accuracy, mean absolute error and simplicity of constructed models.

Year of Publication
2017
Journal
Pattern Analysis and Applications
Volume
20
Number of Pages
441-452
Date Published
May
ISSN Number
1433-755X
URL
https://doi.org/10.1007/s10044-015-0506-y
DOI
10.1007/s10044-015-0506-y
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