The use of cascade-correlation neural networks in university fund raising

B. K. WONG, T. A. BODNOVICH, V. S.-K. LAI

Research output: Journal PublicationsJournal Article (refereed)

4 Citations (Scopus)

Abstract

In recent years, many Colleges and Universities in the USA have been facing a serious financial crisis since many state governments have reduced their support for higher education. There is no doubt that one of the solutions to this crisis depends on the successful implementation of University fund raising programs. Identifying the potential donors is an important part of this process. The objective of this research was to develop a cascade-correlation neural network to predict the types of people who would most likely be potential donors. A comparison of the classification accuracy between neural networks and multiple discriminant analyses (MDA) was also conducted. Our results indicated that neural networks could perform as well as MDA in overall accuracy. Furthermore, neural networks could predict with a lot more accuracy the actual donor (Type I hit) than MDA. Our study is the first published case study on the use of artificial neural networks for University fund raising.
Original languageEnglish
Pages (from-to)913-920
Number of pages8
JournalJournal of the Operational Research Society
Volume51
Issue number8
DOIs
Publication statusPublished - 1 Jan 2000

    Fingerprint

Keywords

  • Cascade-correlation
  • Education
  • Neural network
  • University fund raising

Cite this