Towards positive unlabeled learning for parallel data mining: a random forest framework

Chen Li, Xueliang Hua

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)

Abstract

Parallel computing techniques can greatly facilitate traditional data mining algorithms to efficiently tackle learning tasks that are characterized by high computational complexity and huge amounts of data, to meet the requirement of real-world applications. However, most of these techniques require fully labeled training sets, which is a challenging requirement to meet. In order to address this problem, we investigate widely used Positive and Unlabeled (PU) learning algorithms including PU information gain and a newly developed PU Gini index combining with popular parallel computing framework - Random Forest (RF), thereby enabling parallel data mining to learn from only positive and unlabeled samples. The proposed framework, termed PURF (Positive Unlabeled Random Forest), is able to learn from positive and unlabeled instances and achieve comparable classifcation performance with RF trained by fully labeled data through parallel computing according to experiments on both synthetic and real-world UCI datasets. PURF is a promising framework that facilitates PU learning in parallel data mining and is anticipated to be useful framework in many real-world parallel computing applications with huge amounts of unlabeled data.
Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications
Subtitle of host publication10th International Conference, ADMA 2014, Guilin, China, December 19-21, 2014. Proceedings
EditorsXudong Luo, Jeffrey Xu Yu, Zhi Li
Place of PublicationSwitzerland
PublisherSpringer
Pages573 - 587
Number of pages15
ISBN (Print)9783319147161
DOIs
Publication statusPublished - 2014
EventInternational Conference on Advanced Data Mining and Applications 2014 - Guilin, China
Duration: 19 Dec 201421 Dec 2014
Conference number: 10th
https://link.springer.com/book/10.1007/978-3-319-14717-8

Publication series

NameLecture Notes in Computer Science (LNCS)
PublisherSpringer
Volume8933
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Advanced Data Mining and Applications 2014
Abbreviated titleADMA 2014
CountryChina
CityGuilin
Period19/12/1421/12/14
OtherAdvanced Data Mining and Applications
10th International Conference, ADMA 2014, Guilin, China, December 19-21, 2014. Proceedings
Internet address

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