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http://hdl.handle.net/1942/32466
Title: | A quality measure for multi-label datasets on the Apache Spark framework | Authors: | Sánchez, Ricardo BELLO GARCIA, Marilyn Morell, Carlos Bello, Rafael VANHOOF, Koen |
Issue Date: | 2019 | Source: | Proceedings of the 2nd International Conference of Information Processing CIPI - IOTAI 2019, | Abstract: | In the last years, the amounts of data have increased considerably and therefore, it is becoming more complex to handle these volumes of information. Measuring the data quality is a pivotal aspect to assess the classifier's discriminatory power as the classifiers accuracy heavily depends on the data used to build the model. Multi-label classification is one specific type of classification problem, which has generated an increasing interest in recent years. However, there are no quality measures for multi-label datasets implemented in cluster computing frameworks to evaluate large datasets. This work aims to implement a measure of data quality for multi-label datasets based on Granular Computing under the Apache Spark framework. As a result, it was possible to calculate the values of the quality measure for the datasets, and even in relatively short times. | Keywords: | apache spark;Quality Measure;multi-label classification;Multi-label Classification;Apache Spark;quality measure | Document URI: | http://hdl.handle.net/1942/32466 | Link to publication/dataset: | https://convencion.uclv.cu/event/2nd-international-conference-of-information-processing-cipi-iotai-2019-international-workshop-of-internet-of-things-artificial-intelligence-2019-06-24-2019-06-29-37/track/a-quality-measure-for-multi-label-datasets-on-the-apache-spark-framework-1642 | ISBN: | 9789593123723 | Category: | C1 | Type: | Proceedings Paper | Validations: | vabb 2023 |
Appears in Collections: | Research publications |
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IoT-AI2019-3pag.pdf | Published version | 179.94 kB | Adobe PDF | View/Open |
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