TY - GEN
T1 - Ordinal classification of depression spatial hot-spots of prevalence
AU - Pérez-Ortiz, M.
AU - Gutiérrez, P. A.
AU - García-Alonso, C.
AU - Salvador-Carulla, L.
AU - Salinas-Pérez, J. A.
AU - Hervás-Martínez, C.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - geographical information systems
KW - kernel discriminant learning
KW - ordinal classification
KW - ordinal regression
KW - spatial distribution of illnesses
UR - http://www.scopus.com/inward/record.url?scp=84857548598&partnerID=8YFLogxK
U2 - 10.1109/ISDA.2011.6121817
DO - 10.1109/ISDA.2011.6121817
M3 - Conference contribution
AN - SCOPUS:84857548598
SN - 9781457716751
T3 - International Conference on Intelligent Systems Design and Applications, ISDA
SP - 1170
EP - 1175
BT - Proceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11
T2 - 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11
Y2 - 22 November 2011 through 24 November 2011
ER -
