China 
Africa 
Explore chapters and articles related to this topic
Mixture of Experts Models
Published in Sylvia Frühwirth-Schnatter, Gilles Celeux, Christian P. Robert, Handbook of Mixture Analysis, 2019
Isobel Claire Gormley, Sylvia Frühwirth-Schnatter
Mixture of experts models provide a framework in which covariates may be included in mixture models. This is achieved by modelling the parameters of the mixture model as functions of the concomitant covariates. Given their mixture model foundation, mixture of experts models possess a diverse range of analytic uses, from clustering observations to capturing parameter heterogeneity in cross-sectional data. This chapter focuses on delineating the mixture of experts modelling framework and demonstrates the utility and flexibility of mixture of experts models as an analytic tool.
An intelligent ensemble classification method based on multi-layer perceptron neural network and evolutionary algorithms for breast cancer diagnosis
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2022
Saeed Talatian Azad, Gholamreza Ahmadi, Amin Rezaeipanah
During the last two decades, a variety of studies have been conducted to use data mining techniques to diagnose breast cancer. During the last two decades, a variety of studies have been conducted to use data mining techniques to diagnose breast cancer. Ahmad et al. (2015) used ANN and GA as a predictor of breast cancer. They used GA to simultaneously optimise ANN parameters including weights, effective features subset and number of hidden nodes. Kabir et al. (2010) developed the CAFS algorithm based on ANN. CAFS can determine the number of hidden nodes during the featured selection process. Moreover, this method uses a grouping technique to select features, where the correlation level (Kim & Nevatia, 1999) is the criterion of grouping. Salehi et al. (2020) developed a novel data mining technique for breast cancer survivability using MLP-NN ensemble learners. They used two ensemble learning techniques including stacked generalisation and mixture of experts based on MLP-NN.