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Titlebook: New Frontiers in Mining Complex Patterns; 6th International Wo Annalisa Appice,Corrado Loglisci,Zbigniew W. Ras Conference proceedings 2018

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書目名稱New Frontiers in Mining Complex Patterns
副標(biāo)題6th International Wo
編輯Annalisa Appice,Corrado Loglisci,Zbigniew W. Ras
視頻videohttp://file.papertrans.cn/666/665287/665287.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: New Frontiers in Mining Complex Patterns; 6th International Wo Annalisa Appice,Corrado Loglisci,Zbigniew W. Ras Conference proceedings 2018
描述.This book features a collection of revised and significantly extended versions of the papers accepted for presentation at the 6th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2017, held in conjunction with ECML-PKDD 2017 in Skopje, Macedonia, in September 2017. The book is composed of five parts: feature selection and induction; classification prediction; clustering; pattern discovery; applications..The workshop was aimed at discussing and introducing new algorithmic foundations and representation formalisms in complex pattern discovery. Finally, it encouraged the integration of recent results from existing fields, such as Statistics, Machine Learning and Big Data Analytics..
出版日期Conference proceedings 2018
關(guān)鍵詞artificial intelligence; classification; cluster analysis; data mining; clustering; data stream mining; en
版次1
doihttps://doi.org/10.1007/978-3-319-78680-3
isbn_softcover978-3-319-78679-7
isbn_ebook978-3-319-78680-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

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Density Estimators for Positive-Unlabeled Learning,ough to be applied to many domains by leveraging tools provided by years of research from the probabilistic generative model community. Results on several benchmark datasets show the performance and flexibility of the proposed approach.
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Phenotype Prediction with Semi-supervised Classification Trees,ered here is especially helpful when using single trees, especially when the amount of labeled data ranges from 20 to 40%. Similar improvements can be seen when the presence of the phenotype is very imbalanced.
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