Title: Investigating the
Optimal Number of Attributes to Manage Knowledge Performances
Abstract: Rules are the most
important element
in knowledge extraction. The performance or strength of rules will
determine
how good a model is. Higher accuracy implies that a model is good
and
vise versa. However, the strength of rules depends on
the
attributes. The number of attributes in a rule can influence the
percentages
of accuracy a model. Most machine learning techniques produce a
large
number of rules. The consequence is with large number of rules
generated,
processing time is much longer. This study investigated the
performances
of rules with different lengths of attribute and identified the optimal
number
of rule for a good model. The research performed experiments using
several
data mining techniques. Data of 50 hardware dataset companies which,
contains
31 attributes and 400 records was used. Results showed that in
terms
of number of rules, Genetic Algorithm produced the highest number of
rules
followed by Johnson’s Algorithm and Holte’s 1R. The best
classifier
for extracting rules in this study is VOT (Voting of Object
Tracking).
In terms of performance of rules, best results comes from rules with 30
attributes,
followed by rules with 1 intersection attribute and lastly rules with 3
intersection
attributes. Among the three sets of attributes, the set with 3
attributes
are considered as the best and three (3) has been identified as the
optimal
number of attributes.
Authors: Mohammad Aizat b. Basir and
Faudziah bt. Ahmad