Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Machine Learning - Special issue on inductive transfer
Theoretical models of learning to learn
Learning to learn
Localized Prediction of Continuous Target Variables Using Hierarchical Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Localized Prediction of Continuous Target Variables Using Hierarchical Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Effective localized regression for damage detection in large complex mechanical structures
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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In this paper, we propose a novel technique for the efficientprediction of multiple continuous target variablesfrom high-dimensional and heterogeneous data sets usinga hierarchical clustering approach. The proposed approachconsists of three phases applied recursively:partitioning, localization and prediction. In thepartitioning step, similar target variables are groupedtogether by a clustering algorithm. In the localizationstep, a classification model is used to predict which groupof target variables is of particular interest. If theidentified group of target variables still contains a largenumber of target variables, the partitioning andlocalization steps are repeated recursively and theidentified group is further split into subgroups with moresimilar target variables. When the number of targetvariables per identified subgroup is sufficiently small, thethird step predicts target variables using localized predictionmodels built from only those data records thatcorrespond to the particular subgroup. Experimentsperformed on the problem of damage prediction incomplex mechanical structures indicate that ourproposed hierarchical approach is computationally moreefficient and more accurate than straightforward methodsof predicting each target variable individually orsimultaneously using global prediction models.