Soft computing applied to distributed regression with context-heterogeneity. Journal of Multiple-Valued Logic and Soft Computing, 26(3-5):389-416, 2016. abstract bibtex © 2016 Old City Publishing, Inc.In this paper we present a distributed regression framework to model distributed data with different contexts. Different context is defined as the change of the underlying laws of probability in the distributed sources. Most state of the art methods do not take into account the different context and assume that the data comes from the same statistical distribution. We propose an aggregation scheme for models that are in the same neighborhood in terms of similarity by means of a Soft Computing approach, by using several methodologies like fuzzy membership functions, clustering algorithms, feedfoward neural network, stacked generalization and ensemble approaches. We conduct experiments with synthetic and real data sets to validate our proposal. Our proposed algorithm outperforms other models that follow a traditional approach.
@article{84961687435,
abstract = "© 2016 Old City Publishing, Inc.In this paper we present a distributed regression framework to model distributed data with different contexts. Different context is defined as the change of the underlying laws of probability in the distributed sources. Most state of the art methods do not take into account the different context and assume that the data comes from the same statistical distribution. We propose an aggregation scheme for models that are in the same neighborhood in terms of similarity by means of a Soft Computing approach, by using several methodologies like fuzzy membership functions, clustering algorithms, feedfoward neural network, stacked generalization and ensemble approaches. We conduct experiments with synthetic and real data sets to validate our proposal. Our proposed algorithm outperforms other models that follow a traditional approach.",
number = "3-5",
year = "2016",
title = "Soft computing applied to distributed regression with context-heterogeneity",
volume = "26",
pages = "389-416",
journal = "Journal of Multiple-Valued Logic and Soft Computing"
}
Downloads: 0
{"_id":"z7tAQ8s5pBpen5gZx","bibbaseid":"anonymous-softcomputingappliedtodistributedregressionwithcontextheterogeneity-2016","downloads":0,"creationDate":"2017-03-31T20:15:32.688Z","title":"Soft computing applied to distributed regression with context-heterogeneity","author_short":null,"year":2016,"bibtype":"article","biburl":"https://1fichier.com/?j9cpurkmnv","bibdata":{"bibtype":"article","type":"article","abstract":"© 2016 Old City Publishing, Inc.In this paper we present a distributed regression framework to model distributed data with different contexts. Different context is defined as the change of the underlying laws of probability in the distributed sources. Most state of the art methods do not take into account the different context and assume that the data comes from the same statistical distribution. We propose an aggregation scheme for models that are in the same neighborhood in terms of similarity by means of a Soft Computing approach, by using several methodologies like fuzzy membership functions, clustering algorithms, feedfoward neural network, stacked generalization and ensemble approaches. We conduct experiments with synthetic and real data sets to validate our proposal. Our proposed algorithm outperforms other models that follow a traditional approach.","number":"3-5","year":"2016","title":"Soft computing applied to distributed regression with context-heterogeneity","volume":"26","pages":"389-416","journal":"Journal of Multiple-Valued Logic and Soft Computing","bibtex":"@article{84961687435,\n abstract = \"© 2016 Old City Publishing, Inc.In this paper we present a distributed regression framework to model distributed data with different contexts. Different context is defined as the change of the underlying laws of probability in the distributed sources. Most state of the art methods do not take into account the different context and assume that the data comes from the same statistical distribution. We propose an aggregation scheme for models that are in the same neighborhood in terms of similarity by means of a Soft Computing approach, by using several methodologies like fuzzy membership functions, clustering algorithms, feedfoward neural network, stacked generalization and ensemble approaches. We conduct experiments with synthetic and real data sets to validate our proposal. Our proposed algorithm outperforms other models that follow a traditional approach.\",\n number = \"3-5\",\n year = \"2016\",\n title = \"Soft computing applied to distributed regression with context-heterogeneity\",\n volume = \"26\",\n pages = \"389-416\",\n journal = \"Journal of Multiple-Valued Logic and Soft Computing\"\n}\n\n","key":"84961687435","id":"84961687435","bibbaseid":"anonymous-softcomputingappliedtodistributedregressionwithcontextheterogeneity-2016","urls":{},"downloads":0,"html":""},"search_terms":["soft","computing","applied","distributed","regression","context","heterogeneity"],"keywords":[],"authorIDs":[],"dataSources":["gKiCRHjjC2iGthGEx"]}