@conference{162011,
author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and Carlos Garc{\'i}a-Alonso and L. Salvador Carulla and J.A. Salinas P{\'e}rez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "In this paper we apply and test a recent ordinal
algorithm for classification (Kernel Discriminant Learning
Ordinal Regression, KDLOR), in order to recognize a group
of geographically close spatial units with a similar prevalence
pattern significantly high (or low), which are called hot-
spots (or cold-spots). Different spatial analysis techniques have
been used for studying geographical distribution of a specific
illness in mental health-care because it could be useful to
organize the spatial distribution of health-care services. Ordinal
classification is used in this problem because the classes are:
spatial unit with depression, spatial unit which could present
depression and spatial unit where there is not depression. It is
shown that the proposed method is capable of preserving the
rank of data classes in a projected data space for this database.
In comparison to other standard methods like C4.5, SVMRank,
Adaboost, and MLP nominal classifiers, the proposed KDLOR
algorithm is shown to be competitive.
",
booktitle = "11th International Conference on Intelligent Systems Design andApplications (ISDA 2011)",
keywords = "ordinal regression, spatial distribution of illnesses, geographical information systems, kernel discriminant learning",
pages = "1170-1175",
title = "{O}rdinal classification of depression spatial hot-spots of prevalence",
year = "2011",
}