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EnglishBig Data frameworks allow powerful distributed computations extending the results achievable on a single machine. In this work, we present a novel distributed associative classifier, named BAC, based on ensemble techniques. Ensembles are a popular approach that builds several models on different subsets of the original dataset, eventually voting to provide a unique classification outcome. Experiments on Apache Spark and preliminary results showed the capability of the proposed ensemble classifier to obtain a quality comparable with the single-machine version on popular real-world datasets, and overcome their scalability limits on large synthetic datasets.
BAC: A bagged associative classifier for big data frameworks / Venturini, Luca; Garza, Paolo; Apiletti, Daniele. - STAMPA. - 637(2016), pp. 137-146. ((Intervento presentato al convegno 3rd International Workshop on Big Data Applications and Principles, BigDap 2016, co-located with the 20th East-European Conference on Advances in Databases and Information Systems, ADBIS 2016 tenutosi a Prague, Czech Republic nel 28-8-2016.
| Titolo: | BAC: A bagged associative classifier for big data frameworks |
| Autori: | |
| Data di pubblicazione: | 2016 |
| Abstract: | Big Data frameworks allow powerful distributed computations extending the results achievable on a... single machine. In this work, we present a novel distributed associative classifier, named BAC, based on ensemble techniques. Ensembles are a popular approach that builds several models on different subsets of the original dataset, eventually voting to provide a unique classification outcome. Experiments on Apache Spark and preliminary results showed the capability of the proposed ensemble classifier to obtain a quality comparable with the single-machine version on popular real-world datasets, and overcome their scalability limits on large synthetic datasets. |
| ISBN: | 9783319440651 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| bac-bigdap-paper (17).pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2651083
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