Multiknowledge for Decision Making

Q Wu; DA Bell; T McGinnity

doi:10.1007/s10115-004-0150-0

Multiknowledge for Decision Making

Q Wu, DA Bell, T McGinnity

Faculty Of Computing, Eng. & Built Env.

Research output: Contribution to journal › Article › peer-review

38 Citations (Scopus)

Original language	English
Pages (from-to)	246-266
Journal	Knowledge and Information Systems
Volume	7
Issue number	2
DOIs	https://doi.org/10.1007/s10115-004-0150-0
Publication status	Published - 1 Feb 2005

Access to Document

10.1007/s10115-004-0150-0

Cite this

@article{fe32d938d5ce4efebaf0ce389d71c0a0,

title = "Multiknowledge for Decision Making",

author = "Q Wu and DA Bell and T McGinnity",

note = "Other Details ------------------------------------ A critical aspect of any intelligent system is the representation of knowledge. Frequently knowledge is represented by a mapping from a condition space to a decision space. The large databases utilised are often built for purposes other than knowledge discovery and incorporate substantial redundancies, thus resulting in increased complexity in the knowledge discovery process. The importance of this paper is that it proposes a new multi-knowledge approach which, when combined with the Bayes classifier, allows for a higher decision accuracy, particularly in the situation of data sets with a large number of attributes.",

year = "2005",

month = feb,

day = "1",

doi = "10.1007/s10115-004-0150-0",

language = "English",

volume = "7",

pages = "246--266",

journal = "Knowledge and Information Systems",

issn = "0219-1377",

publisher = "Springer",

number = "2",

}

TY - JOUR

T1 - Multiknowledge for Decision Making

AU - Wu, Q

AU - Bell, DA

AU - McGinnity, T

N1 - Other Details ------------------------------------ A critical aspect of any intelligent system is the representation of knowledge. Frequently knowledge is represented by a mapping from a condition space to a decision space. The large databases utilised are often built for purposes other than knowledge discovery and incorporate substantial redundancies, thus resulting in increased complexity in the knowledge discovery process. The importance of this paper is that it proposes a new multi-knowledge approach which, when combined with the Bayes classifier, allows for a higher decision accuracy, particularly in the situation of data sets with a large number of attributes.

PY - 2005/2/1

Y1 - 2005/2/1

U2 - 10.1007/s10115-004-0150-0

DO - 10.1007/s10115-004-0150-0

M3 - Article

VL - 7

SP - 246

EP - 266

JO - Knowledge and Information Systems

JF - Knowledge and Information Systems

SN - 0219-1377

IS - 2

ER -