Multi-granularity semi-random data partitioning

Han Liu*, Mihaela Cocea

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

In this chapter, we introduce the concepts of semi-heuristic data partitioning, and present a proposed multi-granularity framework for semi-heuristic data partitioning. We also discuss the advantages of the proposed framework in terms of dealing with class imbalance and the sample representativeness issue, from granular computing perspectives.

Original languageEnglish
Title of host publicationGranular Computing Based Machine Learning
PublisherSpringer
Pages49-65
Number of pages17
ISBN (Electronic)9783319700588
ISBN (Print)9783319700571
DOIs
Publication statusPublished - 5 Nov 2017

Publication series

NameStudies in Big Data
PublisherSpringer
Volume35
ISSN (Print)2197-6503
ISSN (Electronic)2197-6511

Fingerprint

Dive into the research topics of 'Multi-granularity semi-random data partitioning'. Together they form a unique fingerprint.

Cite this