Extension of Mixture-of-Experts Networks for Binary Classification of Hierarchical Data

Ng, S. K. and McLachlan, G. J. (2007) Extension of Mixture-of-Experts Networks for Binary Classification of Hierarchical Data. Artificial Intelligence in Medicine, 41 1: 57-67. doi:10.1016/j.artmed.2007.06.001

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Author Ng, S. K.
McLachlan, G. J.
Title Extension of Mixture-of-Experts Networks for Binary Classification of Hierarchical Data
Journal name Artificial Intelligence in Medicine   Check publisher's open access policy
ISSN 0933-3657
Publication date 2007
Sub-type Article (original research)
DOI 10.1016/j.artmed.2007.06.001
Volume 41
Issue 1
Start page 57
End page 67
Total pages 11
Editor Adlassnig, K. P.
Place of publication Netherlands
Publisher Elsevier
Collection year 2008
Language eng
Subject 230204 Applied Statistics
780101 Mathematical sciences
0104 Statistics
Abstract Objective: For many applied problems in the context of medically relevant artificial intelligence, the data collected exhibit a hierarchical or clustered structure. Ignoring the interdependence between hierarchical data can result in misleading classification. In this paper, we extend the mechanism for mixture-of-experts (ME) networks for binary classification of hierarchical data. Another extension is to quantify cluster-specific information on data hierarchy by random effects via the generalized linear mixed-effects model (GLMM). Methods and material: The extension of ME networks is implemented by allowing for correlation in the hierarchical data in both the gating and expert networks via the GLMM. The proposed model is illustrated using a real thyroid disease data set. In our study, we consider 7652 thyroid diagnosis records from 1984 to early 1987 with complete information on 20 attribute values. We obtain 10 independent random splits of the data into a training set and a test set in the proportions 85% and 15%. The test sets are used to assess the generalization performance of the proposed model, based on the percentage of misclassifications. For comparison, the results obtained from the ME network with independence assumption are also included. Results: With the thyroid disease data, the misclassification rate on test sets for the extended ME network is 8.9%, compared to 13.9% for the ME network. In addition, based on model selection methods described in Section 2, a network with two experts is selected. These two expert networks can be considered as modeling two groups of patients with high and low incidence rates. Significant variation among the predicted cluster-specific random effects is detected in the patient group with low incidence rate. Conclusions: It is shown that the extended ME network outperforms the ME network for binary classification of hierarchical data. With the thyroid disease data, useful information on the relative log odds of patients with diagnosed conditions at different periods can be evaluated. This information can be taken into consideration for the assessment of treatment planning of the disease. The proposed extended ME network thus facilitates a more general approach to incorporate data hierarchy mechanism in network modeling. (c) 2007 Elsevier B.V. All rights reserved.
Keyword Computer Science, Artificial Intelligence
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

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Created: Thu, 10 Apr 2008, 14:02:08 EST by Marie Grove on behalf of School of Mathematics & Physics