id: 02199591
dt: a
an: 02199591
au: Greco, Salvatore; Słowiński, Roman; Stefanowski, Jerzy; Żurawski, Marcin
ti: Incremental versus non-incremental rule induction for multicriteria
    classification.
so: Peters, James F. (ed.) et al., Transactions on Rough Sets II. Rough sets
    and fuzzy sets. Berlin: Springer (ISBN 3-540-23990-1/pbk). Lecture
    Notes in Computer Science 3135. Journal Subline, 33-53 (2004).
py: 2004
pu: Berlin: Springer
la: EN
cc: 
ut: rule induction; incremental learning; multiple criteria decision analysis;
    classification and sorting
ci: 
li: doi:10.1007/b100633
ab: Summary: Induction of decision rules within the dominance-based rough set
    approach to the multicriteria and multiattribute classification is
    considered. Within this framework, we discuss two algorithms: Glance
    and an extended version of AllRules. The important characteristics of
    Glance is that it induces the set of all dominance-based rules in an
    incremental way. On the other hand, AllRules induces in a
    non-incremental way the set of all robust rules, i.e. based on objects
    from the set of learning examples. The main aim of this study is to
    compare both these algorithms. We experimentally evaluate them on
    several data sets. The results show that Glance and AllRules are
    complementary algorithms. The first one works very efficiently on data
    sets described by a low number of condition attributes and a high
    number of objects. The other one, conversely, works well on data sets
    characterized by a high number of attributes and a low number of
    objects.
rv: