The PVAD Algorithm to Learn Partial-Value Variable Associations with Application to Modelling for Engineering Retention

Nong Ye, Ting Yan Fok, Xin Wang, James Collofello, Nancy Dickson

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Existing data analytic techniques are mostly based on building the same one model of variable relations over the full ranges of all variable values, although relations of variables may exist only for certain values of variables or different relations exist for different values of variables. This paper presents the Partial-Value Association Discovery (PVAD) algorithm which discovers variable relations/associations that exist in partial ranges of variable values from large amounts of data in a computationally efficient way. The PVAD algorithm allows building a structural model of partial- and full-value variable associations in multiple layers that captures individual and interactive effects of multiple variables by learning from data. The application of the PVAD algorithm to the analysis of engineering student data for engineering retention is also presented.

Original languageEnglish (US)
Pages (from-to)505-510
Number of pages6
JournalIFAC-PapersOnLine
Volume51
Issue number2
DOIs
Publication statusPublished - Jan 1 2018

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Keywords

  • engineering education
  • interactive effects of variables
  • multi-layer model
  • retention
  • variable association

ASJC Scopus subject areas

  • Control and Systems Engineering

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