Feature Selection for Data and Pattern Recognition: An Introduction - Feature Selection for Data and Pattern Recognition - page 4

of weights. The procedures are employed for rule [ 37 ] and connectionist [ 26 ]
classifiers, applied in the task of authorship attribution.
Part II Rough Set Approach to Attribute Reduction
Chapter 6 discusses two probabilistic approaches [ 44 ] to rough sets: the variable
precision rough set model [ 43 ] and the Bayesian rough set model, as they apply
to data dependencies detection, analysis and their representation. The focus is on
the analysis of data co-occurrence-based dependencies appearing in classification
tables and probabilistic decision tables acquired from data. In particular, the notion
of attribute reduct, in the framework of probabilistic approach, is of interest in the
chapter and it includes two efficient reduct computation algorithms.
Chapter 7 provides an introduction to a rough set approach to attribute reduction
[ 1 ], treated as removing condition attributes with preserving some part of the
lower/upper approximations of the decision classes, because the approximations
summarize the classification ability of the condition attributes [ 42 ]. Several types
of reducts according to structures of the approximations are presented, called
“structure-based” reducts. Definitions and theoretical results for structures-based
attribute reduction are given [ 33 , 36 ].
Part III Rule Discovery and Evaluation
Chapter 8 compares a strategy of rule induction based on feature selection [ 32 ],
exemplified by the LEM1 algorithm, with another strategy, not using feature selec-
tion, exemplified by the LEM2 algorithm [ 15 , 16 ]. The LEM2 algorithm uses all
possible attribute-value pairs as the search space. It is shown that LEM2 signifi-
cantly outperforms LEM1, a strategy based on feature selection in terms of an error
rate. The LEM2 algorithm induces smaller rule sets with the smaller total number
of conditions as well. The time complexity for both algorithms is the same [ 31 ].
Chapter 9 addresses action rules extraction. Action rules present users with a set of
actionable tasks to follow to achieve a desired result. The rules are evaluated using
their supporting patterns occurrence and their confidence [ 41 ]. These measures fail
to measure the feature values transition correlation and applicability, hence meta-
actions are used in evaluating action rules, which is presented in terms of likelihood
and execution confidence [ 14 ]. Also an evaluation model of the application of
meta-actions based on cost and satisfaction is given.
Chapter 10 explores the use of a feature subset selection measure, along with a
number of common statistical interestingness measures, via structure-preserving
flat representation for tree-structured data [ 34 , 35 ]. A feature subset selection is
used prior to association rule generation. Once the initial set of rules is obtained,
irrelevant rules are determined as those that are comprised of attributes not deter-
mined to be statistically significant for the classification task [ 22 ].
Part IV Data- and Domain-Oriented Methodologies
Chapter 11 gives a survey of hubness-aware classification methods and instance
selection. The presence of hubs, the instances similar to exceptionally large number